Use of Extended Detection Periods for Range Aliasing Detection and Mitigation in a Light Detection and Ranging (LIDAR) System

ABSTRACT

A computing system may operate a LIDAR device to emit and detect light pulses in accordance with a time sequence including standard detection period(s) that establish a nominal detection range for the LIDAR device and extended detection period(s) having durations longer than those of the standard detection period(s). The system may then make a determination that the LIDAR detected return light pulse(s) during extended detection period(s) that correspond to particular emitted light pulse(s). Responsively, the computing system may determine that the detected return light pulse(s) have detection times relative to corresponding emission times of particular emitted light pulse(s) that are indicative of one or more ranges. Given this, the computing system may make a further determination of whether or not the one or more ranges indicate that an object is positioned outside of the nominal detection range, and may then engage in object detection in accordance with the further determination.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/851,987, filed on Apr. 17, 2020, which is a continuation of U.S.patent application Ser. No. 15/665,591, filed on Aug. 1, 2017, whichapplications are incorporated herein by reference. U.S. patentapplication Ser. No. 15/638,607, filed on Jun. 30, 2017, is alsoincorporated herein by reference.

BACKGROUND

A vehicle can be configured to operate in an autonomous mode in whichthe vehicle navigates through an environment with little or no inputfrom a driver. Such an autonomous vehicle can include one or moresensors that are configured to detect information about the environmentin which the vehicle operates. One such sensor is a light detection andranging (LIDAR) device.

A LIDAR device can estimate distance to environmental features whilescanning through a scene to assemble a “point cloud” indicative ofreflective surfaces in the environment. Individual points in the pointcloud can be determined by transmitting a laser pulse and detecting areturning pulse, if any, reflected from an object in the environment,and determining the distance to the object according to the time delaybetween the transmitted pulse and the reception of the reflected pulse.

A LIDAR device may thus include a laser, or set of lasers, and mayrapidly and repeatedly scan across a scene to provide continuousreal-time information on distances to reflective objects in the scene.Combining the measured distances and the orientation of the laser(s)while measuring each distance allows for associating a three-dimensionalposition with each returning pulse. In this way, a three-dimensional mapof points indicative of locations of reflective features in theenvironment can be generated for the entire scanning zone.

One challenge in using LIDARs can be range aliasing. Range aliasingrelates to the appearance of false echoes, such as when a system cannotdisambiguate between a signal scattered from one particular range and asignal scattered from other ranges based on the generated data. Forexample, in the context of LIDARs, range aliasing can refer a returnsignal from outside a LIDAR's maximum unambiguous range beinginterpreted to be within the LIDAR's maximum unambiguous range.

SUMMARY

Example implementations may relate to methods and system for usingextended detection periods to determine whether or not an object ispositioned outside of a nominal detection range of a LIDAR device.

In particular, a computing system may operate a LIDAR device to emit anddetect light pulses in accordance with a time sequence includingstandard detection period(s) that establish the nominal detection rangefor the LIDAR device and including extended detection period(s) havingdurations longer than those of the standard detection period(s). In thisway, the computing system may extend the detection range of the LIDARdevice during the extended detection periods.

With this arrangement, based on detection of light pulse(s) by the LIDARdevice during these extended detection periods, the computing systemcould determine whether or not the LIDAR device detected return lightpulses that reflected off an object positioned outside of the nominaldetection range of the LIDAR device. Specifically, the computing systemmay determine, respectively for each such detected light pulse, a rangeaccording to a time delay relative to an emission time of a mostrecently emitted light pulse. If the computing system then determinesthat the nominal detection range comprises these determined ranges, thenthe computing system may responsively make a determination that theseranges do not indicate that an object is positioned outside of thenominal detection range. Whereas, if the computing system determinesthat the nominal detection range does not comprise these determinedranges, then the computing system may responsively make a determinationthat these ranges indicate that an object is positioned outside of thenominal detection range.

Once the computing system makes the determination of whether or not theranges indicate that an object is positioned outside of the nominaldetection range, the computing system may then engage in objectdetection accordingly. For example, if the computing system determinesthat the ranges indicate an object is positioned outside of the nominaldetection range, the computing system could then carry out operations toidentify that object and/or to determine a distance to that object. Inanother example, the computing system could use the determination as abasis for overcoming range ambiguity in other detection periods, such asby using the determination as a basis for determining whether or notlight pulses detected in other detection periods reflected off object(s)positioned outside the nominal detection range. In yet another example,the computing system could use the determination as a basis forselectively triggering use of other processes that help overcome rangeambiguity. Other examples are also possible.

In one aspect, a method is disclosed. The method involves operating, bya computing system, a Light Detection and Ranging (LIDAR) device to emitlight pulses at emission times in accordance with an emission timesequence and to detect return light pulses in accordance with adetection time sequence, where the detection time sequence includes, foreach emitted light pulse, a corresponding detection period for detectionof a corresponding return light pulse, and where the correspondingdetection periods comprise (i) one or more standard detection periodsthat establish a nominal detection range for the LIDAR device and (ii)one or more extended detection periods having respective durations thatare longer than respective durations of the one or more standarddetection periods. The method also involves making a determination, bythe computing system, that the LIDAR device detected one or more returnlight pulses during one or more of the extended detection periods thatcorrespond to one or more particular emitted light pulses. The methodadditionally involves, in response to making the determination,determining, by the computing system, that the one or more detectedreturn light pulses have detection times relative to correspondingemission times of the one or more particular emitted light pulses thatare indicative of one or more ranges. The method further involves makinga further determination, by the computing system, of whether or not theone or more ranges indicate that an object is positioned outside of thenominal detection range. The method yet further involves engaging, bythe computing system, in object detection in accordance with the furtherdetermination.

In another aspect, a computing system for a self-driving vehicle isdisclosed. The computing system includes one or more processors, anon-transitory computer readable medium, and program instructions storedon the non-transitory computer readable medium and executable by the oneor more processors. In particular, the program instructions may beexecutable to operate a Light Detection and Ranging (LIDAR) device toemit light pulses at emission times in accordance with an emission timesequence, where the emission time sequence includes a standard timeperiod after a majority of emissions in the sequence and an extendedtime period after at least one of the emissions in the sequence, whereinthe standard time period is associated with a nominal detection rangefor the LIDAR device.

In yet another aspect, a vehicle is disclosed. The vehicle includes aLight Detection and Ranging (LIDAR) device and a computing system. Thecomputing system may be configured to operate the LIDAR device to emitlight pulses at emission times in accordance with an emission timesequence and to detect return light pulses in accordance with adetection time sequence, where the detection time sequence includes, foreach emitted light pulse, a corresponding detection period for detectionof a corresponding return light pulse, and where the correspondingdetection periods comprise (i) one or more standard detection periodsthat establish a nominal detection range for the LIDAR device and (ii)one or more extended detection periods having respective durations thatare longer than respective durations of the one or more standarddetection periods. The computing system may also be configured to make adetermination that the LIDAR device detected one or more return lightpulses during one or more of the extended detection periods thatcorrespond to one or more particular emitted light pulses. The computingsystem may additionally be configured to, in response to making thedetermination, determine that the one or more detected return lightpulses have detection times relative to corresponding emission times ofthe one or more particular emitted light pulses that are indicative ofone or more ranges. The computing system may be further configured tomake a further determination of whether or not the one or more rangesindicate that an object is positioned outside of the nominal detectionrange. The computing system may be yet further configured to engage inobject detection in accordance with the further determination.

In yet another aspect, another system is disclosed. The system mayinclude means for operating a Light Detection and Ranging (LIDAR) deviceto emit light pulses at emission times in accordance with an emissiontime sequence and to detect return light pulses in accordance with adetection time sequence, where the detection time sequence includes, foreach emitted light pulse, a corresponding detection period for detectionof a corresponding return light pulse, and where the correspondingdetection periods comprise (i) one or more standard detection periodsthat establish a nominal detection range for the LIDAR device and (ii)one or more extended detection periods having respective durations thatare longer than respective durations of the one or more standarddetection periods. The system may also include means for making adetermination that the LIDAR device detected one or more return lightpulses during one or more of the extended detection periods thatcorrespond to one or more particular emitted light pulses. The systemmay additionally include means for, in response to making thedetermination, determining that the one or more detected return lightpulses have detection times relative to corresponding emission times ofthe one or more particular emitted light pulses that are indicative ofone or more ranges. The system may further include means for making afurther determination of whether or not the one or more ranges indicatethat an object is positioned outside of the nominal detection range. Thesystem may yet further include means for engaging in object detection inaccordance with the further determination.

These as well as other aspects, advantages, and alternatives will becomeapparent to those of ordinary skill in the art by reading the followingdetailed description with reference where appropriate to theaccompanying drawings. Further, it should be understood that thedescription provided in this summary section and elsewhere in thisdocument is intended to illustrate the claimed subject matter by way ofexample and not by way of limitation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of a LIDAR device, according to anexample embodiment.

FIG. 2A illustrates a LIDAR device, according to an example embodiment.

FIG. 2B illustrates another LIDAR system, according to an exampleembodiment.

FIG. 3A shows several views of a LIDAR device being positioned on top ofa vehicle, according to an example embodiment.

FIG. 3B shows emission of light by a LIDAR device positioned on top ofthe vehicle, according to an example embodiment.

FIG. 3C shows detection of reflected light by a LIDAR device positionedon top of the vehicle, according to an example embodiment.

FIG. 3D shows a scanning range of a LIDAR device positioned on top ofthe vehicle, according to an example embodiment.

FIG. 4A shows a nominal unambiguous detection range of a LIDAR devicepositioned on top of the vehicle, according to an example embodiment.

FIG. 4B shows a pulse reflected off an object positioned within anominal unambiguous detection range of a LIDAR device, according to anexample embodiment.

FIG. 4C shows a pulse reflected off an object positioned outside anominal unambiguous detection range of a LIDAR device, according to anexample embodiment.

FIG. 5A shows a first time sequence and shows multiple possibledetection times for each of a plurality of detected lights pulses,according to an example embodiment.

FIG. 5B shows range ambiguity with respect to the first time sequence,according to an example embodiment.

FIG. 6 is a flowchart illustrating a method for utilizing extendeddetection period(s) in a LIDAR system, according to an exampleembodiment.

FIG. 7A illustrates an extended detection range, according to an exampleembodiment.

FIG. 7B illustrates a second time sequence that includes extendeddetection period(s), according to an example embodiment.

FIG. 7C illustrates use of an extended detection period for determiningthat an object is positioned outside of a nominal detection range of aLIDAR device, according to an example embodiment.

FIG. 7D illustrates use of an extended detection period for overcomingrange ambiguity, according to an example embodiment.

FIG. 8 illustrates operation of a vehicle based on scans of anenvironment received from a LIDAR device, according to an exampleembodiment.

FIG. 9 is a simplified block diagram of a vehicle, according to anexample embodiment.

DETAILED DESCRIPTION

Exemplary methods and systems are described herein. It should beunderstood that the word “exemplary” is used herein to mean “serving asan example, instance, or illustration.” Any implementation or featuredescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations orfeatures. In the figures, similar symbols typically identify similarcomponents, unless context dictates otherwise. The exampleimplementations described herein are not meant to be limiting. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are contemplatedherein.

I. Overview

There are continued efforts to improve autonomous operation in which avehicle navigates through an environment with little or no input from adriver. Such efforts include development of vehicles equipped withremote sensing capabilities and possibly accident-avoidance systems. Forinstance, various sensors, such as a LIDAR device, may be included in avehicle to detect objects in an environment of the vehicle and tothereby facilitate autonomous operation and/or accident avoidance.

Generally, a LIDAR device can help estimate distance(s) to environmentalfeatures while scanning through a scene to assemble a “point cloud”indicative of reflective surfaces in the environment. Individual pointsin the point cloud can be determined by emitting a light pulse anddetecting a returning light pulse, if any, reflected from an object inthe environment, and determining the distance to the object according tothe time delay between the emitted light pulse and the detection of thereflected returning light pulse. A LIDAR can include laser(s) or otherlight sources. The laser(s), or the LIDAR as a whole, can rapidly andrepeatedly scan across a scene to provide continuous real-timeinformation on distances to reflective objects in the scene. With thisarrangement, combining the measured distances and the orientation of thelaser(s) while measuring each distance allows for associating athree-dimensional position with each returning light pulse. In this way,a three-dimensional map of points indicative of locations of reflectivefeatures in the environment can be generated for the entire scanningzone.

When a computing system (e.g., in a vehicle) operates a LIDAR device,the computing system may operate the LIDAR device to emit and detectlight pulses in accordance with certain timing. For example, thecomputing system may operate the LIDAR device to emit light pulses atemission times in accordance with an emission time sequence, such as aperiodic sequence (e.g., emission of a light pulse once everymicrosecond). In this example, the computing system may also operate theLIDAR device to detect return light pulses in accordance with adetection time sequence. The detection time sequence may have adetection period intended to detect a light pulse returned from anobject located within a certain range of the LIDAR. This detectionperiod could be referred to herein as a nominal detection period or astandard detection period, and this range could be referred to herein asa nominal unambiguous detection range or a nominal detection range.

More specifically, a corresponding detection period for a given lightpulse may begin immediately following or at some time after emission ofthat given light pulse, and may end before or after emission of asubsequent light pulse. This corresponding detection period could bearranged for detection of a return light pulse that corresponds to thegiven emitted light pulse reflecting off an object located within thenominal detection range of the LIDAR to result in that correspondingreturn light pulse. In practice, the nominal detection range spans aminimum distance, x₀, to a maximum distance x_(m), from the LIDARdevice. The minimum distance, x₀, may be 0 meters and maximum distancex_(m), may be 60 meters, for example. In other instances, the minimumdistance, x₀, may be a distance >0 m away from the LIDAR where objectdetection is unlikely or would not be an input in the maneuvering of thevehicle, for example. For instance, if the LIDAR is mounted beneath anaircraft, x₀ may be 2 meters. Other distances are also contemplated.Moreover, the minimum distance could be referred to herein as a minimumunambiguous detection range, and the maximum distance could be referredto herein as a maximum unambiguous detection range.

When the LIDAR device detects return light pulses, the computing systemcould generate a range hypothesis for these detected return lightpulses. Specifically, the computing system could determine, respectivelyfor each detected light pulse, a range according to a time delayrelative to an emission time of a most recently emitted light pulse.This range hypothesis may be referred to herein as the close rangehypothesis or the default range hypothesis.

Generally, a light pulse that is reflected off an object positionedoutside of the nominal detection range would not be detected by theLIDAR device within the nominal detection period. For instance, theLIDAR device may not detect such a light pulse if the light pulse'sintensity is significantly attenuated before arriving at the LIDARdevice.

In some situations, however, the LIDAR device may nonetheless detect alight pulse that is reflected off an object positioned outside of thenominal detection range. For example, the object at issue may be aretroreflective object (e.g., a large freeway road sign) positionedbeyond the nominal detection range's maximum distance. When a lightpulse reflects off a retroreflective object located beyond the nominaldetection range, the return light pulse may be detected by the LIDARdevice during a detection period after the nominal detection period,giving rise to range ambiguity. In another example, the object at issuemay be an object positioned closer to the LIDAR device than the nominaldetection range's minimal distance. Consequently, in some scenarios,when a light pulse reflects off that closer object, the LIDAR device maydetect that light pulse during a detection period before the nominaldetection period, also giving rise to range ambiguity.

Disclosed herein is an approach that can help a computing systemdetermine whether or not a LIDAR device detected return light pulsesthat reflected off an object positioned outside the nominal detectionrange. In accordance with the disclosed approach, the computing systemcould be arranged to extend respective durations of one or moredetection periods. Given this, the computing system may in turn extendthe detection range of the LIDAR device during these extended detectionperiods. As such, based on detection of light pulse(s) by the LIDARdevice during these extended detection periods, the computing systemcould determine whether or not the LIDAR device detected return lightpulses that reflected off an object positioned outside of the nominaldetection range of the LIDAR device.

In particular, the computing system may operate the LIDAR device to haveone or more standard detection periods and one or more extendeddetection periods. The standard detection period may be those thatestablish the nominal detection range for the LIDAR device, in line withthe discussion above. The extended detection periods may have respectivedurations that are longer than respective durations of the standarddetection periods, thereby temporarily expanding the detection range ofthe LIDAR device during those extended detection periods.

In an example implementation, the extended detection periods could bearranged to occur at any feasible time. By way of example, the extendeddetection periods could take place in accordance with a fixed schedule,such as by taking place periodically or non-periodically. For instance,one in every 64 detection periods could be extended, and the remainingdetection periods could be standard detection periods. In otherexamples, however, the extended detection periods may not take place inaccordance with a fixed schedule. For instance, the computing systemcould selectively extend one or more detection periods based on one ormore factors. Other examples are also possible.

With this arrangement, when the LIDAR device detects return lightpulse(s) during extended detection period(s) associated with particularemitted light pulse(s), the computing system could then responsivelydetermine ranges associated with these detections and use these rangesas basis for determining whether or not the LIDAR device detected lightpulses that reflected off an object positioned outside of the nominaldetection range.

More specifically, the computing system could determine that thedetected return light pulses have detection times relative tocorresponding emission times of the particular emitted light pulses thatare indicative of one or more ranges. Based on a comparison of thesedetermined ranges to the nominal detection range, the computing systemcould then make a determination of whether or not these ranges indicatethat an object is positioned outside of the nominal detection range ofthe LIDAR device.

By way of example, the computing system may determine whether or not thedetermined ranges are greater than the above-mentioned maximumunambiguous detection range of the LIDAR device. If the computing systemdetermines that the determined ranges are not greater than the maximumunambiguous detection range, then the computing system may responsivelydetermine that the detected light pulses reflected off an objectpositioned within the nominal detection range, and thus that the rangesdo not indicate that an object is positioned outside of the nominaldetection range (e.g., assuming a minimum unambiguous detection range of0 m). However, if the computing system determines that the determinedranges are greater than the maximum unambiguous detection range, thenthe computing system may responsively determine that the detected lightpulses reflected off an object positioned beyond the maximum unambiguousdetection range, and thus that the ranges indicate that an object ispositioned outside of the nominal detection range. Other examples arealso possible.

Once the computing system evaluates light pulse(s) detected during theextended detection period(s) and makes a determination of whether or notthe ranges indicate that an object is positioned outside of the nominaldetection range, the computing system may then engage in objectdetection accordingly. For example, if the computing system determinesthat the ranges indicate an object that is positioned beyond the maximumdetection range, the computing system could use one or more techniquesto identify the object and/or to determine a distance to that object,among other options. In another example, the computing system could usethe determination as a basis for overcoming range ambiguity in otherdetection periods. For example, the computing system could use thedetermination as basis for determining whether or not light pulsesdetected in other detection periods reflected off object(s) positionedoutside the nominal detection range. In any case, such detection ofobject(s) could in turn help the computing system optimize autonomousoperation of a vehicle, among other outcomes.

In this manner, the disclosed approach could help reduce the extent ofcomputation often carried out to determine whether or not an object ispositioned outside of a nominal detection range of a LIDAR device. Forinstance, if a LIDAR device is operated to only have standard detectionperiods, then range ambiguity may arise if during such standarddetection periods the LIDAR device detects return light pulses thatreflected off an object positioned outside of the nominal detectionrange. And although certain processes could help overcome rangeambiguity and/or help detect objects positioned outside of the nominaldetection range, such processes could be computationally costly.Therefore, given that the disclosed approach could help overcome theseissues by sparsely extending respective durations of detectionperiod(s), the disclosed approach could help avoid use of such processesand/or could serve as a guide for selectively triggering use of suchprocesses, and thus could ultimately help reduce the extent ofcomputational resources being used by a computing system.

II. Example Arrangement of a LIDAR Device

Referring now to the Figures, FIG. 1 is a simplified block diagram of aLIDAR device 100, according to an example embodiment. As shown, theLIDAR device 100 includes a power supply arrangement 102, electronics104, light source(s) 106, at least one transmitter 108, at least onereceiver 110, a rotating platform 112, actuator(s) 114, a stationaryplatform 116, a connector arrangement 118, a rotary link 120, and ahousing 122. In other embodiments, the LIDAR device 100 may includemore, fewer, or different components. Additionally, the components shownmay be combined or divided in any number of ways.

Power supply arrangement 102 may be configured to supply power tovarious components of the LIDAR device 100. In particular, the powersupply arrangement 102 may include or otherwise take the form of atleast one power source disposed within the LIDAR device 100 andconnected to various components of the LIDAR device 100 in any feasiblemanner, so as to supply power to those components. Additionally oralternatively, the power supply arrangement 102 may include or otherwisetake the form of a power adapter or the like that is configured toreceive power from one or more external power sources (e.g., from apower source arranged in a vehicle to which the LIDAR device 100 iscoupled) and to supply that received power to various components of theLIDAR device 100 in any feasible manner. In either case, any type ofpower source may be used such as, for example, a battery.

Electronics 104 may include one or more electronic components and/orsystems each arranged to help facilitate certain respective operationsof the LIDAR device 100. In practice, these electronics 104 may bedisposed within the LIDAR device 100 in any feasible manner. Forinstance, at least some of the electronics 104 may be disposed within acentral cavity region of the rotary link 120. Nonetheless, theelectronics 104 may include various types of electronic componentsand/or systems.

For example, the electronics 104 may include various wirings used fortransfer of control signals from a computing system to variouscomponents of the LIDAR device 100 and/or for transfer of data fromvarious components of the LIDAR device 100 to the computing system.Generally, the data that the computing system receives may includesensor data based on detections of light by the receiver 110, amongother possibilities. Moreover, the control signals sent by the computingsystem may operate various components of the LIDAR device 100, such asby controlling emission of light by the transmitter 106, controllingdetection of light by the receiver 110, and/or controlling theactuator(s) 114 to rotate the rotating platform 112, among otherpossibilities.

In some arrangements, the electronics 104 may also include a computingsystem. This computing system may have one or more processors, datastorage, and program instructions stored on the data storage andexecutable by the one or more processor to facilitate variousoperations. With this arrangement, the computing system may thus beconfigured to carry out operations described herein, such as those ofmethods described below. Additionally or alternatively, the computingsystem may communicate with an external computing system, controlsystem, or the like (e.g., a computing system arranged in a vehicle towhich the LIDAR device 100 is coupled) so as to help facilitate transferof control signals and/or data between the external system and variouscomponents of the LIDAR device 100.

In other arrangements, however, the electronics 104 may not include acomputing system. Rather, at least some of the above-mentioned wiringsmay be used for connectivity to an external computing system. With thisarrangement, the wirings may help facilitate transfer of control signalsand/or data between the external computing system and the variouscomponents of the LIDAR device 100. Other arrangements are possible aswell.

Further, one or more light sources 106 can be configured to emit,respectively, a plurality of light beams and/or pulses havingwavelengths within a wavelength range. The wavelength range could, forexample, be in the ultraviolet, visible, and/or infrared portions of theelectromagnetic spectrum. In some examples, the wavelength range can bea narrow wavelength range, such as provided by lasers.

In practice, one of the light sources 106 may be a laser diodeconfigured to emit pulses of light. In particular, a laser diode may bea semiconductor device including a p-n junction with an active region inwhich oppositely polarized, energized charge carriers (e.g., freeelectrons and/or holes) recombine while current flows through the deviceacross the p-n junction. The recombination results in emission of lightdue to a change in energy state of the charge carriers. When the activeregion is heavily populated by such energized pairs (e.g., the activeregion may have a population inversion of energized states), stimulatedemission across the active region may produce a substantially coherentwave front of light that is then emitted from the laser diode.Recombination events, and the resulting light emission, occur inresponse to current flowing through the device, and so applying a pulseof current to the laser diode results in emission of a pulse of lightfrom the laser diode.

As such, the present disclosure will be generally described herein inthe context of a laser diode being used as the primary light source 106.In some arrangements, however, the one or more light sources 106 mayadditionally or alternatively include fiber lasers, light emittingdiodes (LED), vertical cavity surface emitting lasers (VCSEL), organiclight emitting diodes (OLED), polymer light emitting diodes (PLED),light emitting polymers (LEP), liquid crystal displays (LCD),microelectromechanical systems (MEMS), and/or any other deviceconfigured to selectively transmit, reflect, and/or emit light toprovide the plurality of emitted light beams and/or pulses.

Furthermore, transmitter 108 may be configured to emit light into anenvironment. In particular, the transmitter 108 may include an opticalarrangement that is arranged to direct light from a light source 106toward the environment. This optical arrangement may include anyfeasible combination of mirror(s) used to guide propagation of the lightthroughout physical space and/or lens(es) used to adjust certaincharacteristics of the light, among other optical components. Forinstance, the optical arrangement may include a transmit lens arrangedto collimate the light, thereby resulting in light having rays that aresubstantially parallel to one another. Moreover, the lens may be shapedto spread or otherwise scatter light in a particular manner, such as bycausing the vertical light spread of +7° away from a horizontal axis to−18° away from the horizontal axis (e.g., the horizontal axis ideallybeing parallel to a ground surface in the environment) for example.

As noted, the LIDAR device 100 may include at least one receiver 110.The receiver 110 may be respectively configured to at least detect lighthaving wavelengths in the same wavelength range as the one of the lightemitted from the transmitter 108. In doing so, the receiver 110 maydetect light with a particular resolution. For example, the receiver 110may be configured to detect light with a 0.036° (horizontal)×0.067°(vertical) angular resolution. Moreover, the receiver 110 may beconfigured to scan the environment with a particular FOV. For example,the receiver 110 may be arranged to focus incoming light within a rangeof +7° away from the above-mentioned horizontal axis to −18° away fromthe horizontal axis. In this way, the receiver 110 allows for detectionof light along a range of +7° to −18°, which matches the above-mentionedexemplary vertical spread of emitted light that the transmitter 108provides. It is noted that this resolution and FOV are described forexemplary purposes only and are not meant to be limiting.

In an example implementation, the receiver 110 may have an opticalarrangement that allows the receiver 110 to provide the resolution andFOV as described above. Generally, such an optical arrangement may bearranged to provide an optical path between at least one optical lensand a photodetector array.

More specifically, the receiver 110 may include an optical lens arrangedto focus light reflected from one or more objects in the environment ofthe LIDAR device 100 onto detectors of the receiver 110. To do so, theoptical lens may have certain dimensions (e.g., approximately 10 cm×5cm) as well as a certain focal length (e.g., approximately 35 cm).Moreover, the optical lens may be shaped so as to focus incoming lightalong a particular vertical FOV as described above (e.g., +7° to −18°).Such shaping of the first receiver's optical lens may take on one ofvarious forms (e.g., spherical shaping) without departing from the scopeof the present disclosure.

Furthermore, as noted, the receiver 110 may have a photodetector array,which may include two or more detectors each configured to convertdetected light (e.g., in the above-mentioned wavelength range) into anelectrical signal indicative of the detected light. In practice, such aphotodetector array could be arranged in one of various ways. Forexample, the detectors can be disposed on one or more substrates (e.g.,printed circuit boards (PCBs), flexible PCBs, etc.) and arranged todetect incoming light that is traveling along the optical path from theoptical lens. Also, such a photodetector array could include anyfeasible number of detectors aligned in any feasible manner. Forexample, the photodetector array may include a 13×16 array of detectors.It is noted that this photodetector array is described for exemplarypurposes only and is not meant to be limiting.

Generally, the detectors of the array may take various forms. Forexample, the detectors may take the form of photodiodes, avalanchephotodiodes (e.g., geiger mode and/or linear mode avalanchephotodiodes), phototransistors, cameras, active pixel sensors (APS),charge coupled devices (CCD), cryogenic detectors, and/or any othersensor of light configured to receive focused light having wavelengthsin the wavelength range of the emitted light. Other examples arepossible as well.

Further, as noted, the LIDAR device 100 may include a rotating platform112 that is configured to rotate about an axis. In order to rotate inthis manner, one or more actuators 114 may actuate the rotating platform112. In practice, these actuators 114 may include motors, pneumaticactuators, hydraulic pistons, and/or piezoelectric actuators, amongother possibilities.

In an example implementation, the transmitter 108 and the receiver 110may be arranged on the rotating platform 112 such that each of thesecomponents moves relative to the environment based on rotation of therotating platform 112. In particular, each of these components could berotated relative to an axis so that the LIDAR device 100 may obtaininformation from various directions. In this manner, the LIDAR device100 may have a horizontal viewing direction that can be adjusted byactuating the rotating platform 112 to different directions.

With this arrangement, a computing system could direct an actuator 114to rotate the rotating platform 112 in various ways so as to obtaininformation about the environment in various ways. In particular, therotating platform 112 could rotate at various extents and in eitherdirection. For example, the rotating platform 112 may carry out fullrevolutions such that the LIDAR device 100 provides a 360° horizontalFOV of the environment. Thus, given that the receiver 110 may rotatebased on rotation of the rotating platform 112, the receiver 110 mayhave a horizontal FOV (e.g., 360° or less) and also a vertical FOV asdescribed above.

Moreover, the rotating platform 112 could rotate at various rates so asto cause LIDAR device 100 to scan the environment at various refreshrates. For example, the LIDAR device 100 may be configured to have arefresh rate of 15 Hz (e.g., fifteen complete rotations of the LIDARdevice 100 per second). In this example, assuming that the LIDAR device100 is coupled to a vehicle as further described below, the scanningthus involves scanning a 360° FOV around the vehicle fifteen times everysecond. Other examples are also possible. For example, the rotatingplatform 112 could swivel the LIDAR device so that it scans back andforth within a smaller angle horizontal FOV.

Yet further, as noted, the LIDAR device 100 may include a stationaryplatform 116. In practice, the stationary platform 116 may take on anyshape or form and may be configured for coupling to various structures,such as to a top of a vehicle for example. Also, the coupling of thestationary platform 116 may be carried out via any feasible connectorarrangement 118 (e.g., bolts, screws, and/or adhesives). In this way,the LIDAR device 100 could be coupled to a structure so as to be usedfor various purposes, such as those described herein.

Furthermore, the LIDAR device 100 may also include a rotary link 120that directly or indirectly couples the stationary platform 116 to therotating platform 112. Specifically, the rotary link 120 may take on anyshape, form and material that provides for rotation of the rotatingplatform 112 about an axis relative to the stationary platform 116. Forinstance, the rotary link 120 may take the form of a shaft or the likethat rotates based on actuation from an actuator 114, therebytransferring mechanical forces from the actuator 114 to the rotatingplatform 112. Moreover, as noted, the rotary link 120 may have a centralcavity in which electronics 104 and/or one or more other components ofthe LIDAR device 100 may be disposed. Other arrangements are possible aswell.

Yet further, as noted, the LIDAR device 100 may include a housing 122.In practice, the housing 122 may take on any shape and form. Forexample, the housing 122 can be a dome-shaped housing, among otherpossibilities. Moreover, the housing 122 may be arranged in various waysrelative to other components of the LIDAR device 100. It is noted thatthis housing is described for exemplary purposes only and is not meantto be limiting.

In an example implementation, the housing 122 may be coupled to therotating platform 112 such that the housing 122 is configured to rotateabout the above-mentioned axis based on rotation of the rotatingplatform 112. With this implementation, the transmitter 108, thereceiver 110, and possibly other components of the LIDAR device 100 mayeach be disposed within the housing 122. In this manner, the transmitter108 and the receiver 110 may rotate along with this housing 122 whilebeing disposed within the housing 122.

Moreover, the housing 122 may have an aperture formed thereon, whichcould take on any feasible shape and size. In this regard, thetransmitter 108 could be arranged within the housing 122 so as to emitlight into the environment through the aperture. In this way, thetransmitter 108 may rotate along with the aperture due to correspondingrotation of the housing 122, thereby allowing for emission of light intovarious directions. Also, the receiver 110 could be arranged within thehousing 122 so as to detect light that enters the housing 122 from theenvironment through the aperture. In this way, the receiver 110 mayrotate along with the aperture due to corresponding rotating of thehousing 122, thereby allowing for detection of the light incoming fromvarious directions along the horizontal FOV.

Yet further, the housing 122 may be composed of a material that is atleast partially non-transparent, except for the aperture, which could becomposed of a transparent material. In this way, light could propagatethrough the aperture, thereby allowing for scanning of the environment.But due to the housing 122 being at least partially non-transparent, thehousing 122 may block at least some light from entering the interiorspace of the housing 122 and thus may help mitigate thermal effects. Forinstance, the housing 122 may block sun rays from entering the interiorspace of the housing 122, which may help avoid overheating of variouscomponents of the LIDAR device 100 due to those sun rays. Moreover, dueto various components of the LIDAR device 100 being disposed within thehousing 122 and due to the housing 122 rotating along with thosecomponents, the housing 122 may help protect those components fromvarious environmental hazards, such as rain and/or snow, among others.

In other implementations, however, the housing 122 may be an exteriorstationary housing that does not rotate with the LIDAR device 100. Forexample, the exterior stationary housing could be coupled to a vehicleand the LIDAR device could also be coupled to the vehicle while beingconfigured to rotate within the exterior stationary housing. In thissituation, the exterior stationary housing would likely be transparentso as to allow for propagation of light through the exterior stationaryhousing and thus for scanning of the environment by the LIDAR device100. Moreover, the LIDAR device 100 may also include an aperture throughwhich light may propagate and such an aperture may be on an interiorhousing of the LIDAR device 100, which may rotate within the exteriorstationary housing along with other components of the LIDAR device 100.Other implementations are possible as well.

III. Illustrative Implementation of the LIDAR Device

FIG. 2A illustrates a LIDAR device 200, according to an exampleembodiment. LIDAR 200 may be similar to LIDAR 100. For example, asshown, LIDAR device 200 includes a lens 208, a rotating platform 216, astationary platform 220, and a housing 224 which may be similar,respectively, to optical element 108, rotating platform 216, stationaryplatform 120, and housing 124. Additionally, as shown, light beams 280emitted by LIDAR device 200 propagate from lens 108 along a pointingdirection of LIDAR 200 toward an environment of LIDAR device 200, andreflect off one or more objects in the environment as reflected light290.

In some examples, housing 224 can be configured to have a substantiallycylindrical shape and to rotate about an axis of LIDAR device 200. Inone example, housing 224 can have a diameter of approximately 10centimeters. Other examples are possible. In some examples, the axis ofrotation of LIDAR device 200 is substantially vertical. For instance, byrotating housing 224 that includes the various components athree-dimensional map of a 360-degree view of the environment of LIDARdevice 200 can be determined. Additionally or alternatively, in someexamples, LIDAR device 200 can be configured to tilt the axis ofrotation of housing 224 to control a field of view of LIDAR device 200.Thus, in some examples, rotating platform 216 may comprise a movableplatform that may tilt in one or more directions to change the axis ofrotation of LIDAR device 200.

In some examples, lens 208 can have an optical power to both collimatethe emitted light beams 280, and focus the reflected light 290 from oneor more objects in the environment of LIDAR device 200 onto detectors inLIDAR device 200. In one example, lens 208 has a focal length ofapproximately 120 mm. Other example focal lengths are possible. By usingthe same lens 208 to perform both of these functions, instead of atransmit lens for collimating and a receive lens for focusing,advantages with respect to size, cost, and/or complexity can beprovided. Alternatively, LIDAR 200 may include separate transmit andreceive lenses.

FIG. 2B illustrates another possible implementation of a LIDAR system,according to an example embodiment. As shown, a LIDAR system 228 couldinclude a first LIDAR 230, a second LIDAR 232, a dividing structure 234,and light filter 236.

In some examples, the first LIDAR 230 may be configured to scan anenvironment around a vehicle by rotating about an axis (e.g., verticalaxis, etc.) continuously while emitting one or more light pulses anddetecting reflected light pulses off objects in the environment of thevehicle, for example. In some embodiments, the first LIDAR 230 may beconfigured to repeatedly rotate about the axis to be able to scan theenvironment at a sufficiently high refresh rate to quickly detect motionof objects in the environment. For instance, the first LIDAR 230 mayhave a refresh rate of 10 Hz (e.g., ten complete rotations of the firstLIDAR 230 per second), thereby scanning a 360-degree FOV around thevehicle ten times every second. Through this process, for instance, a 3Dmap of the surrounding environment may be determined based on data fromthe first LIDAR 230. In one embodiment, the first LIDAR 230 may includea plurality of light sources that emit 64 laser beams having awavelength of 905 nm. In this embodiment, the 3D map determined based onthe data from the first LIDAR 230 may have a 0.2° (horizontal)×0.3°(vertical) angular resolution, and the first LIDAR 230 may have a 360°(horizontal)×20° (vertical) FOV of the environment. In this embodiment,the 3D map may have sufficient resolution to detect or identify objectswithin a medium range of 100 meters from a vehicle, for example.However, other configurations (e.g., number of light sources, angularresolution, wavelength, range, etc.) are possible as well.

Unlike the first LIDAR 230, in some embodiments, the second LIDAR 232may be configured to scan a narrower FOV of the environment around avehicle. For instance, the second LIDAR 232 may be configured to rotate(horizontally) for less than a complete rotation about a similar axis.Further, in some examples, the second LIDAR 232 may have a lower refreshrate than the first LIDAR 230. Through this process, a vehicle maydetermine a 3D map of the narrower FOV of the environment using the datafrom the second LIDAR 232. The 3D map in this case may have a higherangular resolution than the corresponding 3D map determined based on thedata from the first LIDAR 230, and may thus allowdetection/identification of objects that are further than the mediumrange of distances of the first LIDAR 230, as well as identification ofsmaller objects within the medium range of distances. In one embodiment,the second LIDAR 232 may have a FOV of 8° (horizontal)×15° (vertical), arefresh rate of 4 Hz, and may emit one narrow beam having a wavelengthof 1550 nm. In this embodiment, the 3D map determined based on the datafrom the second LIDAR 232 may have an angular resolution of 0.1°(horizontal)×0.03° (vertical), thereby allowing detection/identificationof objects within a range of around three hundred meters from a vehicle.However, other configurations (e.g., number of light sources, angularresolution, wavelength, range, etc.) are possible as well.

In some examples, a vehicle may be configured to adjust a viewingdirection of the second LIDAR 232. For example, while the second LIDAR232 has a narrow horizontal FOV (e.g., 8 degrees), the second LIDAR 232may be mounted to a stepper motor (not shown) that allows adjusting theviewing direction of the second LIDAR 232 to pointing directions otherthan that shown in FIG. 1B. Thus, in some examples, the second LIDAR 232may be steerable to scan the narrow FOV along any pointing directionfrom a vehicle.

The dividing structure 234 may be formed from any solid materialsuitable for supporting the first LIDAR 230 and/or optically isolatingthe first LIDAR 230 from the second LIDAR 232. Example materials mayinclude metals, plastics, foam, among other possibilities.

The light filter 236 may be formed from any material that issubstantially transparent to light having wavelengths with a wavelengthrange, and substantially opaque to light having wavelengths outside thewavelength range. For example, the light filter 236 may allow lighthaving the first wavelength of the first LIDAR 230 (e.g., 905 nm) andthe second wavelength of the second LIDAR 232 (e.g., 1550 nm) topropagate through the light filter 236. As shown, the light filter 236is shaped to enclose the first LIDAR 230 and the second LIDAR 232. Thus,in some examples, the light filter 236 may also be configured to preventenvironmental damage to the first LIDAR 230 and the second LIDAR 232,such as accumulation of dust or collision with airborne debris, amongother possibilities. In some examples, the light filter 236 may beconfigured to reduce visible light propagating through the light filter236. In turn, the light filter 236 may improve an aesthetic appearanceof a vehicle by enclosing the first LIDAR 230 and the second LIDAR 232,while reducing visibility of the components of the sensor unit 228 froma perspective of an outside observer, for example. In other examples,the light filter 236 may be configured to allow visible light as well asthe light from the first LIDAR 230 and the second LIDAR 232.

In some embodiments, portions of the light filter 236 may be configuredto allow different wavelength ranges to propagate through the lightfilter 236. For example, an upper portion of the light filter 236 abovethe dividing structure 234 may be configured to allow propagation oflight within a first wavelength range that includes the first wavelengthof the first LIDAR 230. Further, for example, a lower portion of thelight filter 236 below the dividing structure 234 may be configured toallow propagation of light within a second wavelength range thatincludes the second wavelength of the second LIDAR 232. In otherembodiments, the wavelength range associated with the light filter 236may include both the first wavelength of the first LIDAR 230 and thesecond wavelength of the second LIDAR 232.

FIGS. 3A to 3D next collectively illustrate implementation of a LIDARdevice in a vehicle 300, specifically illustrating an implementation ofthe example LIDAR device 200 in the vehicle 300. Although vehicle 300 isillustrated as a car, other embodiments are possible. Furthermore,although the example vehicle 300 is shown as a vehicle that may beconfigured to operate in autonomous mode, the embodiments describedherein are also applicable to vehicles that are not configured tooperate autonomously. Thus, the example vehicle 300 is not meant to belimiting.

In particular, FIG. 3A shows a Right Side View, Front View, Back View,and Top View of the vehicle 300. As shown, the vehicle 300 includes theLIDAR device 200 being positioned on a top side of the vehicle 300opposite a bottom side on which wheels 302 of the vehicle 300 arelocated. Although the LIDAR device 200 is shown and described as beingpositioned on the top side of the vehicle 300, the LIDAR device 200could be positioned on any part feasible portion of the vehicle withoutdeparting from the scope of the present disclosure.

Moreover, FIGS. 3B to 3C next show that the LIDAR device 200 may beconfigured to scan an environment around the vehicle 300 (e.g., at arefresh rate of 15 Hz) by rotating about a vertical axis 308 whileemitting one or more light pulses and detecting reflected light pulsesoff objects in the environment of the vehicle 300, for example.

More specifically, FIG. 3B shows that the LIDAR device 200 emits lightwith the above-mentioned vertical spread of +7° to −18°. In this way,the light emissions can be emitted toward regions of the environmentthat are relatively close to the vehicle 300 (e.g., a lane marker)and/or towards regions of the environment that are further away from thevehicle 300 (e.g., a road sign ahead of the vehicle).

Further, FIG. 3C shows that the LIDAR device 200 may detect reflectedlight with the above-mentioned vertical FOV of +7° to −18° and do so ata resolution of 0.036°×0.067°. In this way, the LIDAR device 200 maydetect light reflected off regions of the environment that arerelatively close to the vehicle 300 and/or light reflected off regionsof the environment that are further away from the vehicle 300.

Generally, these detection distances are illustrated by way of examplein FIG. 3D. In particular, FIG. 3D illustrates a top view of the vehicle300 in the above-described scenario where the vehicle 300 uses the LIDARdevice 200 for scanning a surrounding environment. Accordingly, thehorizontal FOV of the LIDAR device 200 may span 360° in all directionsaround the vehicle 300.

As shown in FIG. 3D, the LIDAR device 200 may be suitable for detectionand/or identification of objects within a range of distances to thevehicle 300. More specifically, objects outside of contour 304 andwithin a range of distances defined by the contour 306 may be properlydetected/identified using the data from the LIDAR device 200. It isnoted that these contours are not to scale but are illustrated as shownfor convenience of description.

IV. Nominal Detection Range and Range Ambiguity

Given that a LIDAR device may be suitable for detection of objectswithin a range of distances, the LIDAR device may have a nominaldetection range that spans from a minimum unambiguous detection range toa maximum unambiguous detection range. For a given detection period ofthe LIDAR device, the maximum unambiguous detection range may define thegreatest distance at which an object can be positioned away from theLIDAR device and be detected by the LIDAR device within the givendetection period, as light pulses reflected from objects past themaximum unambiguous detection range may return to the LIDAR device afterthe given detection period ends. In contrast, for the given detectionperiod, the minimum unambiguous detection range may define the minimumdistance at which an object should be positioned away from the LIDARdevice in order to be detected by the LIDAR device within the givendetection period, as light pulses reflected from objects closer than theminimum distance may return to the LIDAR device before the givendetection period begins.

More specifically, a computing system may operate the LIDAR device toemit and detect light pulses in accordance with certain timing. Forexample, the computing system may operate the LIDAR device to emit lightpulses at emission times in accordance with an emission time sequence,which could be predefined or pseudo-random. This emission time sequencemay then establish a detection time sequence according to which theLIDAR device detects return light pulses.

For instance, once the computing system operates the LIDAR device toemit a given light pulse, a corresponding detection period for the givenlight pulse may begin immediately following or at some time afteremission of that given light pulse, and may end before or after emissionof a subsequent light pulse, among other options. During thiscorresponding detection period, the LIDAR device could then detect agiven return light pulse that corresponds to the given emitted lightpulse, such as when the given emitted light pulse reflects off an objectwithin the nominal detection range to result in that return light pulse.After the LIDAR device detects the given return light pulse, thecomputing system could then determine a specific range associated withthe given return light pulse according to a time delay relative to theemission time of the given emitted light pulse.

As noted, a detection period may establish a nominal detection rangethat spans from a minimum unambiguous detection range to a maximumunambiguous detection range.

In particular, the time difference between the emission time of a lightpulse and the end time of the detection period may correspond to amaximum time delay that a return light pulse from that emitted lightpulse could have in order to still be detected by the LIDAR deviceduring the detection period. For instance, if a detection period begins1 nanosecond after emission of a light pulse and ends 400 nanoseconds(ns) after emission of that light pulse, in order for a return lightpulse from that emitted light pulse to be detected by the LIDAR deviceduring the nominal detection period, this light pulse should return tothe LIDAR device within 400 ns. Further, because a computing systemcould determine a distance to an object according to a time delaybetween emission time of a light pulse and detection time of a reflectedreturning light pulse, the maximum time delay may establish the greatestdistance at which an object could be positioned away from the LIDARdevice, such that the LIDAR device could still detect during thedetection period a light pulse that reflected off this object and thenreturned to the LIDAR. Generally, this greatest distance may define themaximum unambiguous detection range for the detection period.

Additionally, the time difference between emission time of a light pulseand the start time of the detection period may correspond to a minimumtime delay that a return light pulse should have in order to be detectedby the LIDAR device during the nominal detection period. For instance,if a detection period starts 50 nanoseconds (ns) after emission of alight pulse, in order for a return light pulse to be detected by theLIDAR device during that detection period after the light pulse isemitted by the LIDAR device, this light pulse may have to return to theLIDAR device after no less than 50 ns. Further, because a computingsystem could determine a distance to an object according to a time delaybetween emission time of a light pulse and detection of a reflectedreturning light pulse, the minimum time delay may establish the minimumdistance at which an object should be positioned away from the LIDARdevice, such that the LIDAR device could still detect during thedetection period a light pulse that reflected off this object and thenreturned to the LIDAR. Generally, this minimum distance may define theminimum unambiguous detection range for the detection period.

With this arrangement, if a light pulse is reflected off an objectpositioned outside the nominal detection range, a computing system maynot determine a range associated with that light pulse or coulddetermine an incorrect range associated with that light pulse.

By way of example, in many situations, if a light pulse is reflected offan object positioned beyond a maximum unambiguous detection range, theLIDAR device may not detect such a light pulse, as this light pulse mayexperience a significant attenuation in its intensity before arriving atthe LIDAR device. Consequently, the computing system may not determine arange associated with that light pulse.

In some situations, however, the LIDAR device may nonetheless detectthat returning light pulse. For instance, the object positioned beyondthe maximum unambiguous detection range may be a retroreflective object,such as a large road sign located beyond the maximum unambiguousdetection range. A return light pulse that has reflected off such aretroreflective object may be detected by the LIDAR device during asubsequent detection period. Namely, when an emitted light pulse isreflected off a retroreflective object positioned beyond the maximumunambiguous detection range, the LIDAR device may detect this lightpulse at a time after the device has stopped listening for a returnsignal from that emitted light pulse and instead at a time the device islistening for return signals from a subsequently emitted light pulse.Given this, the computing system may calculate the distance the lighttraveled based on the emission time of a later emitted pulse, because itwas not expected to receive an un-attenuated return signal from anobject located past the maximum unambiguous detection range. As aresult, without range aliasing/ambiguity resilience, the computingsystem may erroneously determine that the retroreflective object iscloser than it physically is from the LIDAR device

In another example, in some situations, if a light pulse is reflectedoff an object positioned closer than a minimum unambiguous detectionrange, the LIDAR device may or may not detect such a light pulse. But ifthe LIDAR device does detect such a light pulse, that light pulse mayarrive at the LIDAR device before start of the detection period, andthus the LIDAR device may not detect that light pulse in the detectionperiod associated with that light pulse. Namely, when an emitted lightpulse is reflected off an object positioned closer than the minimumunambiguous detection range, the LIDAR device could possibly detect thislight pulse at a time before the device has started listening for areturn signal from that emitted light pulse and instead at a time thedevice is listening for return signals from a previously emitted lightpulse. As a result, the computing system may not determine a distanceassociated with this light pulse according to a time delay relative toan emission time of that light pulse.

FIGS. 4A-4C illustrate a nominal detection range of the LIDAR device200. In particular, FIGS. 4A-4C show that the LIDAR device 200 may havea nominal detection range that spans from a minimum unambiguousdetection range of 0 meters to a maximum unambiguous detection range 400of 60 meters (60 m). In this example, this maximum unambiguous detectionrange 400 is established by the detection period 408 that startsfollowing an emission time 406A of a light pulse and ends at asubsequent emission time 406B of a subsequent light pulse. As shown, thedetection period 408 has a duration of 400 ns, which leads to themaximum unambiguous detection range 400 to be at approximately 60 m awayfrom the LIDAR device 200 (maximum unambiguous detection range*2=speedof pulse*detection period=˜299,792,458 m/s*400 ns).

Further, FIGS. 4A-4C illustrate that a nearby object 402 (e.g., a nearbyroad sign) could be positioned within the maximum unambiguous detectionrange 400 and that a distant object 404 (e.g., a retroreflective“freeway” road sign) could be positioned outside of the maximumunambiguous detection range 400. In this regard, FIG. 4B shows that apulse reflected off the nearby object 402 would return to the LIDARdevice 200 before the end of the detection period 408 and would do so ata detection time 410 of 350 ns after the emission time 406A. Thisdetection time 410 corresponds to a range of 52.5 m, which is thedistance at which the nearby object 402 is positioned away from theLIDAR device 200. In contrast, distant object 404 is positioned at adistance of 80 m away from the LIDAR device 200, which is a distancethat exceeds the maximum unambiguous detection range 400 of 60 m.Therefore, as shown in FIG. 4C, a pulse reflected off the distant object404 would return to the LIDAR device 200 after the end of the detectionperiod 408 and thus would not be detected by the LIDAR device 200 duringthat detection period 408. Other illustrations are possible as well.

Given that a light pulse could reflect off an object positioned outsideof a nominal detection range of a LIDAR device and then be detected bythe LIDAR device, a computing system could encounter rangealiasing/ambiguity. In particular, when the computing system determinesthat the LIDAR device detected a return light pulse, the computingsystem could determine a range for that return light pulse according toa time delay relative to an emission time of a most recently emittedlight pulse, or could determine a range for that return light pulseaccording to a time delay relative to an emission time of anotheremitted light pulse. But without additional information, the computingsystem may be unable to determine with certainty which of these rangesis the correct range, which could give rise to range ambiguity, therebypossibly leading to false object detections, among other outcomes.

FIGS. 5A to 5B illustrate a scenario that could lead to range ambiguity.

In particular, FIG. 5A shows light pulses A-F emitted respectively atemission times A-F in accordance with a periodic time sequence #1. Theseperiodic emission times establish detection periods A-F each of the same400 ns duration. As shown, light pulses A-F each reflect off the distantobject 404 and, as a result, are each respectively detected during asubsequent detection period.

Generally, the computing system could determine candidate rangesassociated with detected light pulses A-F without accounting for thepossibility of large retroreflective object(s) located beyond themaximum unambiguous detection range. For instance, the computing systemmay determine that the LIDAR device 200 detected light pulse A at adetection time Tn0 of 133 ns relative to emission time B, whichcorresponds to a range of 20 m as shown in FIG. 5B. And as indicated bydetection times Tn1 to Tn5, a similar approach could be used fordetermining ranges associated with light pulses B-F, thereby resultingin first ranges 502 corresponding to a close range hypothesis of anobject being positioned at 20 m away from the LIDAR device 200.

Given this, the computing system determines that ranges 502 are the sameas one another and/or that ranges 502 assemble a point cloudrepresentative of an object and, as a result, could determine that theseranges 502 should be used as basis for further object detection (e.g.,identification of the object or establishing a distance to the object asan average of the ranges 502). However, this close range hypothesis isinaccurate, as light pulses A-F in fact reflected off the distant object404 that is positioned beyond the maximum unambiguous detection range ofthe LIDAR device 200. Thus, use of this close range hypothesis forobject detection could lead to a false detection of a nearby object.

In some implementations, the computing system may also determine thatthe LIDAR device 200 detected light pulse A at a detection time Tf0 of533 ns relative to emission time A, which corresponds to a range of 80 mas shown in FIG. 5B. And as indicated by detection times Tf1 to Tf5, asimilar approach could be used for determining ranges associated withlight pulses B-F, thereby resulting in second ranges 504 correspondingto a “far range hypothesis” of an object being positioned at 80 m awayfrom the LIDAR device 200. This far range hypothesis is accurate, aslight pulses A-F in fact reflected off the distant object 404 that ispositioned beyond the maximum detection range of the LIDAR device 200.

However, although this far range hypothesis is accurate, the computingsystem may be unable to disambiguate between the close and far rangehypotheses. Specifically, the computing system may determine a closerange hypothesis including ranges 502 that are the same as one anotherand/or that assemble a point cloud representative of an object, and mayin turn determine that this indicates an object is positioned at 20 maway from the LIDAR device 200. Additionally, the computing system maydetermine a far range hypothesis including ranges 504 that are the sameas one another and/or that also assemble a point cloud representative ofan object, and may in turn determine that this indicates an object ispositioned at 80 m away from the LIDAR device 200. As a result, thecomputing system may determine that an object could be positioned at 20m away from the LIDAR device 200 or at 80 m away from the LIDAR device200. But without additional information, the computing system may beunable to determine which of these determinations is in fact accurate,thereby leading to range ambiguity. Other illustrations are alsopossible.

Generally, a computing system could carry out one or more rangealiasing/ambiguity resilience techniques to help overcome rangeambiguity and to possibly detect an object position outside of a nominaldetection range of a LIDAR device. An example of such a techniqueinvolves application of time-varying dither to the emission timesequence as well as generation and evaluation of multiple rangehypotheses. This technique is described in detail in application Ser.No. 15/638,607, which is incorporated herein by reference.

In accordance with the technique described in application Ser. No.15/638,607, to help resolve range ambiguity, a computing system couldoperate a LIDAR device to emit light pulses in accordance with a timesequence that includes a time-varying dither, and, once return lightpulses are detected, could generate and evaluate multiple rangehypotheses. In some examples, one of the range hypotheses could be aclose range hypotheses, and the computing system could generate one ormore alternate range hypotheses in addition to the close rangehypothesis. In other examples, instead of generating a close rangehypothesis, the computing system could generate two or more alternaterange hypotheses.

In this regard, various alternate range hypotheses are possible. By wayof example, for each detected light pulse, the computing system coulddetermine a range based on the difference between the detection time anda time a light pulse was emitted prior to the last emitted light pulse.In this example, the alternate range hypothesis could be referred to asa far range hypothesis, as the determined range corresponds to thepossibility of an object being positioned beyond the maximum unambiguousdetection range.

As such, when the computing system determines that the LIDAR devicedetected return light pulses during two or more detection periods, thecomputing system may determine (i) a first set of ranges in accordancewith a time delay relative to corresponding emission times of aplurality of first emitted light pulses and (ii) a second set of rangesin accordance with a time delay relative to corresponding emission timesof a plurality of second emitted light pulses.

Based on one or more factors, the computing system could then selectbetween using the first set of ranges as a basis for object detectionand using the second set of ranges as a basis for object detection. Forinstance, due to the application of time-varying dither, a rangehypothesis that is incorrect would include a set of ranges that do notresemble any known object or are otherwise substantially different fromone another. Whereas, despite application of time-varying dither, arange hypothesis that is correct could still include a set of rangesthat resemble a known object or are otherwise substantially similar fromone another. Therefore, the computing system could evaluate resemblanceto known object(s) and/or similarity of ranges as basis for selectingbetween the sets of ranges.

By way of example, the computing system may determine that the first setof ranges closely resembles a known object and that the second set ofranges does not resemble any known objects, and the system mayresponsively select the first set of ranges to be used as basis forobject detection. In another example, the system may determine that thefirst set includes ranges that are substantially similar to one anotherand that the second set includes ranges that are substantially differentfrom one another, and the system may responsively select the first setof ranges to be used as basis for object detection.

As such, given this technique, the computing system could determine theappropriate ranges to use for basis for object detection, even whendetected return light pulse(s) are light pulses that reflect off anobject positioned outside the nominal detection range.

V. Utilization of Extended Detection Periods in a LIDAR System

FIG. 6 is a flowchart illustrating a method 600, according to an exampleimplementation. In particular, method 600 may be implemented to helpdetermine whether or not an object might be positioned outside of anominal detection range of a LIDAR device, and to then engage in objectdetection accordingly.

Method 600 shown in FIG. 6 (and other processes and methods disclosedherein) presents a method that can be implemented within an arrangementinvolving, for example, the LIDAR device 100 of FIG. 1, vehicle 300shown in FIGS. 3A-3D, and/or vehicle 900 shown in FIG. 9 and furtherdescribed below (or more particularly by one or more components orsubsystems thereof, such as by a processor and a non-transitorycomputer-readable medium having instructions that are executable tocause the device to perform functions described herein). Additionally oralternatively, method 600 may be implemented within any otherarrangements and systems.

Method 600 and other processes and methods disclosed herein may includeone or more operations, functions, or actions as illustrated by one ormore of blocks 602-610. Although the blocks are illustrated insequential order, these blocks may also be performed in parallel, and/orin a different order than those described herein. Also, the variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or removed based upon the desired implementation.

In addition, for the method 600 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of the present disclosure. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer readable medium, forexample, such as a storage device including a disk or hard drive. Thecomputer readable medium may include non-transitory computer readablemedium, for example, such as computer-readable media that stores datafor short periods of time like register memory, processor cache andRandom Access Memory (RAM). The computer readable medium may alsoinclude non-transitory media, such as secondary or persistent long termstorage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device. Inaddition, for the method 600 and other processes and methods disclosedherein, each block in FIG. 6 may represent circuitry that is wired toperform the specific logical functions in the process.

At block 602, method 600 involves operating a Light Detection andRanging (LIDAR) device to emit light pulses at emission times inaccordance with an emission time sequence and to detect return lightpulses in accordance with a detection time sequence, where the detectiontime sequence includes, for each emitted light pulse, a correspondingdetection period for detection of a corresponding return light pulse,and where the corresponding detection periods comprise (i) one or morestandard detection periods that establish a nominal detection range forthe LIDAR device and (ii) one or more extended detection periods havingrespective durations that are longer than respective durations of theone or more standard detection periods.

A computing system could operate a LIDAR device to emit and detect lightpulses in accordance with certain timing. For instance, the computingsystem could operate the LIDAR device to emit light pulses in accordancewith an emission time sequence, which could be a periodic emission timesequence or a non-periodic emission time sequence. In any case, theemission time sequence may help establish a detection time sequenceaccording to which the LIDAR device detects return light pulses.

Specifically, the detection time sequence may include a correspondingdetection period respectively for each emitted light pulse. Inparticular, a corresponding detection period for a given light pulse maybegin immediately following or at some time after emission of that givenlight pulse, and may end before or after emission of a subsequent lightpulse. During this corresponding detection period, the LIDAR devicecould detect a given return light pulse that corresponds to the givenemitted light pulse, such as when the given emitted light pulse reflectsoff an object to result in that return light pulse. After the LIDARdevice detects the given return light pulse at a certain detection time,the computing system could then determine a range to an object thatreflected the given emitted light pulse. As discussed, the computingsystem could determine this range according to a time delay between thedetection time of the given return light pulse and the emission time ofthe given emitted light pulse.

In accordance with the present disclosure, a LIDAR device's detectionand emission time sequence could be arranged so as to include one ormore standard detection periods and one or more extended detectionperiods. As further discussed herein, the extended detection period(s)may have respective durations that are longer than respective durationsof the standard detection period(s).

Generally, a standard detection period may begin and end in accordancecertain timing relative to a light pulse emission. In practice, astandard detection period may begin at a start time that is within afirst “standard-period” time frame after emission of a light pulse. Byway of example, a start time of a standard detection period could be setto be anywhere between 2 ns and 4 ns after the light pulse emission(e.g., could be set at 3 ns after the light pulse emission).Additionally, a standard detection period may end at an end time that iswithin a second “standard-period” time frame after emission of a lightpulse. By way of example, an end time of a standard detection periodcould be set to be anywhere between 390 ns and 410 ns after the lightpulse emission (e.g., could be set at 400 ns after the light pulseemission).

When a time sequence is arranged to include multiple such standarddetection periods, some or all of the standard detection periods couldbe the same as one another and/or some or all of the standard detectionperiods could be different from one another. For example, some or all ofthe standard detection periods could each have a start time of 3 nsafter a corresponding light pulse emission and an end time of 400 nsafter a corresponding light pulse emission. In another example, one ofthe standard detection periods could have a start time of 2.5 ns after acorresponding light pulse emission and an end time of 395 ns after thecorresponding light pulse emission. Whereas, another one of the standarddetection periods could have a start time of 3.2 ns after acorresponding light pulse emission and an end time of 405 ns after thecorresponding light pulse emission. In this example, despite thesedetection periods having different start and end times, these detectionperiods would still be considered to be standard detection periods, astheir start and ends times respectively fall within the above-mentionedfirst and second standard-period time frames.

In this regard, standard detection periods may establish the nominaldetection range of the LIDAR device. In particular, if all of thestandard detection periods are the same as one another, then the startand end times of these detection periods may establish the nominaldetection range in accordance with the discussion above. However, if oneor more of the standard detection periods are different than otherstandard detection period(s), then different standard detection periodscould have different respective nominal detection ranges. In this case,the nominal detection range of the LIDAR define could define thedistances at which an object can be positioned away from the LIDARdevice and be reliably detected by the LIDAR device when taking allstandard detection periods of the LIDAR device into consideration.

In particular, the standard detection period having an end time thatprovides for the greatest maximum time delay relative to a light pulseemission may establish a maximum detection range of the LIDAR device,and the standard detection period having the start time that providesfor the smallest minimum time delay relative to a light pulse emissionmay establish a minimum unambiguous detection range of the LIDAR device.Other arrangements of standard detection periods are possible as well.

In contrast, an extended detection period may begin and end inaccordance with certain timing relative to a light pulse emission, butthat timing may cause the extended detection period to be of a longerduration than any one of the standard detection period(s). Inparticular, an extended detection period may begin at a start time thatis within a first “extended-period” time frame after emission of a lightpulse, with the first “extended-period” time frame being arrangedearlier in time relative to a light pulse emission compared to timing ofthe first “standard-period” time frame. Additionally or alternatively,an extended detection period may end at an end time that is within asecond “extended-period” time frame after emission of a light pulse,with the second “extended-period” time frame being arranged later intime relative to a light pulse emission compared to timing of the second“standard-period” time frame.

By way of example, a start time of an extended detection period could beset to be anywhere between 0 ns and 2 ns after a corresponding lightpulse emission (e.g., could be set at lns after the light pulseemission), which is earlier in time than the above-mentioned example“standard-period” time frame of 2 ns to 4 ns after the light pulseemission. Additionally or alternatively, an end time of an extendeddetection period could be set to be anywhere between 410 ns and 650 nsafter a corresponding light pulse emission (e.g., could be set at 600 nsafter the light pulse emission), which is later in time than theabove-mentioned example “standard-period” time frame of 390 ns to 410 nsafter the light pulse emission. Other examples are also possible.

When a time sequence is arranged to include multiple such extendeddetection periods, some or all of the extended detection periods couldbe the same as one another and/or some or all of the extended detectionperiods could be different from one another. For example, some or all ofthe extended detection periods could each have a start time of 1 nsafter a corresponding light pulse emission and an end time of 600 nsafter a corresponding light pulse emission. In another example, one ofthe extended detection periods could have a start time of 1.5 ns after acorresponding light pulse emission and an end time of 550 ns after thecorresponding light pulse emission. Whereas, another one of the extendeddetection periods could have a start time of 1.7 ns after acorresponding light pulse emission and an end time of 570 ns after thecorresponding light pulse emission. In this example, despite thesedetection periods having different start and end times, these detectionperiods would still be considered to be extended detection periods, astheir start and ends times respectively fall within the above-mentionedfirst and second extended-period time frames.

With this arrangement, the extended detection period(s) may fromtime-to-time help extend the detection range of the LIDAR device. Forexample, if an extended detection period is arranged to have arespective end time (i.e., relative to a light pulse emissioncorresponding to the extended detection period) that is later in timethan a standard detection period's respective end time (i.e., relativeto a light pulse emission corresponding to the standard detectionperiod), then the LIDAR device could detect during such extendeddetection period light pulse(s) that reflect off object(s) positionedbeyond the maximum unambiguous detection range established by thestandard detection period. In another example, if an extended detectionperiod is arranged to have a respective start time (i.e., relative to alight pulse emission corresponding to the extended detection period)that is earlier in time than a standard detection period's respectivestart time (i.e., relative to a light pulse emission corresponding tothe standard detection period), then the LIDAR device could detectduring such extended detection period light pulse(s) that reflect offobject(s) positioned closer than the minimum unambiguous detection rangeestablished by the standard detection period. Other arrangements ofextended detection periods are possible as well.

Given an arrangement including extended and standard detection periods,the computing system could be arranged to emit and detect light pulsesin accordance with a fixed schedule. In particular, the computing systemmay have stored thereon or may otherwise have access to a fixed schedulethat indicates timing to respectively initiate and end the detectionperiods. For instance, the fixed schedule could specify a start timeand/or an end time respectively for each extended detection period. Inanother instance, the fixed schedule could specify a start time and/oran end time respectively for each standard detection period. Moreover,the fixed schedule could specify how frequently and/or when extendeddetection periods should occur in a time sequence relative to standarddetection periods. For example, the fixed schedule could specify that anextended detection period should be followed by ten standard detectionperiods, that these ten standard detection periods should be followed byanother extended detection period, and that this other extendeddetection period should then be followed by another eight standarddetection periods, and so on.

In other implementations, however, the computing system could bearranged to dynamically utilize extended detection periods in accordancewith factors other than a fixed schedule. For instance, the computingsystem could determine information about an environment of an autonomousvehicle, such as based on data received from the autonomous vehicle'ssensor system (e.g., from sensor(s) other than a LIDAR device). Based onthe environment information, the computing system could then determinewhether or not to operate the LIDAR device so as to enable or otherwiseincrease use of extended detection periods. In a specific example, thecomputing system could use data from a Global Positioning System (GPS)as basis to determine that the autonomous vehicle is entering a highway.And given that highways tend to include many retroreflective objects,the computing system could respond to the data from the GPS by enablingor otherwise increasing use of extended detection periods while theautonomous vehicle is driving on the highway. Other examples are alsopossible.

Regardless of whether or not the computing system operates the LIDARdevice according to a fixed schedule, the computing system could bearranged to sparsely utilize extended detection periods during operationof the LIDAR device. Specifically, the computing system could bearranged to operate the LIDAR device such that standard detectionperiods occur more frequently over time compared to occurrences ofextended detection periods. In this way, the computing system couldadvantageously utilize the extended detection periods as furtherdescribed herein, and the more frequent occurrences of standarddetection periods could provide for sufficiently high sampling ratesduring operation of the LIDAR device.

In this regard, the computing system could operate the LIDAR device suchthat extended detection periods occur periodically or non-periodicallyover time. Specifically, the computing system could operate the LIDARdevice to initiate extended detection periods in accordance with aperiodic sequence in which one in every X emitted light pulses has acorresponding extended detection period, with X being representative ofa particular quantity. By way of example, the computing system couldoperate the LIDAR device such that one in 64 emitted light pulses has acorresponding extended detection period. In another arrangement,however, the computing system could operate the LIDAR device to initiateextended detection periods in accordance with a non-periodic sequence.For instance, the computing system could operate the LIDAR device suchthat extended detection periods occur in accordance with a pseudo-randomsequence that is based on application of time-varying dither.

Furthermore, in some implementations, the computing system could operatethe LIDAR device such that, when extended detection periods do occur,those extended detection periods correspond to emitted light pulses thatare more prone to reflecting off an object positioned outside of thenominal detection range. In this way, when the LIDAR device does detectreturn light pulse(s) that reflected off object(s) positioned outside ofthe nominal detection range, there may be an increased likelihood ofsuch return light pulse(s) being detected during extended detectionperiod(s). This may in turn increase utilization of the extendeddetection periods for purposes of determining whether or not an objectmight be positioned outside of a nominal detection range, as furtherdiscussed herein.

By way of example, the computing system could operate the LIDAR devicesuch that, when extended detection periods do occur, those extendeddetection periods correspond to light pulses emitted in one or moreparticular directions of travel. For instance, one such particulardirection of travel could be a direction of travel that is substantiallyparallel to or elevated away from a ground surface, such as a groundsurface (e.g., road) on which an autonomous vehicle is traveling. Inthis way, the computing system could operate the LIDAR device so as toavoid occurrences of extended detection periods that correspond to lightpulses emitted towards the ground surface, as such emitted light pulsesare less likely to reflect off an object positioned outside of thenominal detection range. As such, in practice, the computing systemcould operate the LIDAR device to emit a light pulse in one of theparticular directions of travel at issue, and emission of that lightpulse could be followed by an extended detection period arranged fordetection of that light pulse once it returns as a result of beingreflected off an object, which could be an object positioned outside ofthe nominal detection range. Other implementations are also possible.

At block 604, method 600 involves making a determination that the LIDARdevice detected one or more return light pulses during one or more ofthe extended detection periods that correspond to one or more particularemitted light pulses.

In accordance with the present disclosure, the computing system couldmake a determination that the LIDAR device detected one or more returnlight pulses respectively during each of one or more extended detectionperiods. For instance, a plurality of emitted light pulses could eachrespectively have a corresponding extended detection period. For eachsuch corresponding extended detection period, the computing system coulddetermine that the LIDAR device detected one or more return lightpulses. In practice, some or all of these light pulses could be lightpulses that reflected off object(s) positioned outside the nominaldetection range of the LIDAR device. Additionally or alternatively, someor all of these light pulses could be light pulses that reflected offnearby object(s) positioned within the nominal detection range.

In some cases, the computing system could additionally determine thatthe LIDAR device detected one or more return light pulses respectivelyduring each of one or more standard detection periods. For instance, aplurality of emitted light pulses could each respectively have acorresponding standard detection period. For each such correspondingstandard detection period, the computing system could determine that theLIDAR device detected one or more return light pulses. Here again, someor all of these light pulses could be light pulses that reflected offobject(s) positioned outside the nominal detection range of the LIDARdevice. Additionally or alternatively, some or all of these light pulsescould be light pulses that reflected off nearby object(s) positionedwithin the nominal detection range.

In any case, as further discussed herein, detection of light pulse(s)during extended detection period(s) could help a computing systemdetermine whether or not an object might be positioned outside thenominal detection range of the LIDAR device. And if the LIDAR devicealso detects light pulse(s) during standard detection period(s), thenlight pulse detection(s) during extended detection periods(s) could helpa computing system overcome range ambiguity that could arise withregards to the light pulse detection(s) during standard detectionperiod(s).

At block 606, method 600 involves, in response to making thedetermination, determining that the one or more detected return lightpulses have detection times relative to corresponding emission times ofthe one or more particular emitted light pulses that are indicative ofone or more ranges.

Once the computing system determines that the LIDAR device detectedreturn light pulse(s) during extended detection period(s), the computingsystem may responsively generate a range hypothesis for these detectedreturn light pulse(s). Specifically, the computing system may determinea range respectively for each detected return light pulse according to atime delay relative to an emission time of a most recently emitted lightpulse. For example, after a given light pulse is emitted at a particularemission time and then detected during an extended detection period at aparticular detection time, the computing system could then determine arange for that light pulse according to a time delay between theparticular emission time and the particular detection time.

When the computing system determines a range for a light pulse detectedduring an extended detection period, this range determination can bemore accurate than a range determination for a light pulse detectedduring a standard detection period.

Specifically, if a light pulse has a corresponding standard detectionperiod and that light pulse is reflected off an object positionedoutside of the nominal detection range, that light pulse would not bedetected by the LIDAR device during its corresponding standard period.In this scenario, as discussed, the computing system could end updetermining a range for that light pulse according to a time delayrelative to an emission time of a different light pulse (e.g., asubsequently emitted light pulse), which would lead to an incorrectrange determination.

On the other hand, if that same light pulse had a corresponding extendeddetection period and similarly reflected off the object positionedoutside of the nominal detection range, that light pulse is likely to bedetected during its corresponding extended detection period. In otherwords, the extended detection period effectively extends the detectionrange of the LIDAR device to be beyond (further and/or closer than) thenominal detection range. Further, if the LIDAR device detects that lightpulse during its corresponding extended detection period, the computingsystem could determine a range for that light pulse according to a timedelay relative to an emission time of the light pulse, which wouldamount to a correct range determination.

At block 608, method 600 involves making a further determination ofwhether or not the one or more ranges indicate that an object ispositioned outside of the nominal detection range.

Given that a range determination for a light pulse detected during anextended detection period is more likely to be correct, the computingsystem could use light pulse(s) detected during extended detectionperiod(s) to help determine whether or not an object might be positionedoutside of the nominal detection range of the LIDAR device. Inparticular, once the computing system determines one or more ranges forlight pulse(s) detected during extended detection period(s), thecomputing system may make a further determination of whether or notthese one or more ranges indicate that an object is positioned outsideof the nominal detection range, and the computing system could do so invarious ways.

In one example implementation, the computing system could make thefurther determination through a comparison of these one or more rangesto the nominal detection range. In particular, the computing system maydetermine whether or not the nominal detection range comprises the oneor more ranges. If the computing system determines that the nominaldetection range comprises the one or more ranges, then the computingsystem may responsively determine that the one or more ranges do notindicate that an object is positioned outside of the nominal detectionrange. In this case, the computing system could further responsivelydetermine that the one or more ranges indicate that an object ispositioned within the nominal detection range. On the other hand, if thecomputing system determines that the nominal detection range does notcomprise the one or more ranges, then the computing system mayresponsively determine that the one or more ranges indicate that anobject is positioned outside of the nominal detection range.

By way of example, the LIDAR device may detect a light pulse during anextended detection period and the computing system may determine a rangeof 92 m for that light pulse according to a time delay relative to anemission time of that light pulse. The computing system may then comparethat determined range to a nominal detection range spanning from 2 m to60 m. In this example, the computing system may determine that thenominal detection range does not comprise the determined range. Inparticular, the range of 92 m is outside of the nominal detection rangespanning from 2 m to 60 m. As such, the computing system mayresponsively determine that the determined range of 92 m indicates thatan object is positioned outside of the nominal detection range.

In another example, the LIDAR device may detect a light pulse during anextended detection period and the computing system may determine a rangeof 18 m for that light pulse according to a time delay relative to anemission time of that light pulse. The computing system may then comparethat determined range to the nominal detection range spanning from 2 mto 60 m. In this example, the computing system may determine that thenominal detection range comprises the determined range. In particular,the range of 18 m is within the nominal detection range spanning from 2m to 60 m. As such, the computing system may responsively determine thatthe determined range of 18 m does not indicate that an object ispositioned outside of the nominal detection range. In this case, thecomputing system could further responsively determine that thedetermined range of 18 m indicates that an object is positioned withinthe nominal detection range. Other examples are also possible.

In a further aspect, when the computing system determines that the oneor more ranges indicate that an object is positioned outside of thenominal detection range, the computing system could more specificallydetermine whether that object might be positioned closer than thenominal detection range or whether that object might be positionedbeyond the nominal detection range. The computing system could do so invarious ways.

In one example implementation, the computing system may determinewhether the one or more ranges are less than the minimum unambiguousdetection range or whether the one or more ranges are greater than themaximum unambiguous detection range. If the computing system determinesthat the one or more ranges are less than the minimum unambiguousdetection range, then the computing system may responsively determinethat the one or more ranges indicate that an object is positioned closerthan the nominal detection range. On the other hand, if the computingsystem determines that the one or more ranges are greater than themaximum unambiguous detection range, then the computing system mayresponsively determine that the one or more ranges indicate that anobject is positioned beyond the nominal detection range. Otherimplementations are also possible.

At block 610, method 600 involves engaging in object detection inaccordance with the further determination.

Once the computing system makes the further determination of whether ornot the ranges indicate that an object is positioned outside of thenominal detection range, the computing system may then engage in objectdetection accordingly.

In an example implementation, engaging in object detection in accordancewith the further determination could involve using the furtherdetermination as a basis for overcoming range ambiguity, such as forpurposes of determining appropriate ranges to use for further objectdetection (e.g., identification of an object).

Generally, the computing system could determine that the LIDAR devicedetected other return light pulses, such as other than those detectedduring the above-mentioned extended detection period(s), and could thenresponsively generate multiple range hypotheses for these other returnlight pulses. However, in line with the discussion above, the computingsystem may encounter range ambiguity, as it may be unclear withoutadditional information which of these range hypotheses is correct andwhich of these range hypotheses is incorrect.

In a specific example, the computing system could determine that theLIDAR device detected return light pulses during standard detectionperiod(s) that are substantially close in time to the above-mentionedextended detection period(s) during which light pulse(s) were detectedas discussed with regards to block 604. In practice, these could be oneor more standard detection periods that immediately follow and/orimmediately precede one of the extended detection periods, for instance.In this example, the computing system could in turn responsivelydetermine that (i) the detected other return light pulses have detectiontimes relative to corresponding emission times of a plurality of firstemitted light pulses that are indicative of a first set of ranges and(ii) the detected other return light pulses have detection timesrelative to corresponding emission times of a plurality of secondemitted light pulses that are indicative of a second set of ranges.However, range ambiguity may arise as it may be unclear as to whetherthe first or second set of ranges should be used as a basis for objectdetection.

In accordance with the present disclosure, the computing system couldovercome such range ambiguity based on evaluation of light pulsesdetection(s) during extended detection period(s). In particular, oncethe computing system determines range(s) for light pulse(s) detectedduring extended detection period(s) and makes the further determinationof whether or not these range(s) indicate that an object is positionedoutside of the nominal detection range, the computing system could thenuse this further determination as a basis to select between using thefirst set of ranges and using the second set of range for objectdetection.

More specifically, if the further determination is that the range(s) donot indicate an object is positioned outside of the nominal detectionrange, this may further serve as an indication that the light pulsesdetected during the close-in-time standard detection period(s) are morelikely to be light pulses that reflected off an object positioned withinthe nominal detection range, and thus may ultimately serve as anindication that a close range hypothesis is more likely to be correct.Whereas, if the further determination is that the range(s) indicate anobject is positioned outside of the nominal detection range, this mayfurther serve as an indication that the light pulses detected during theclose-in-time standard detection period(s) are more likely to be lightpulses that reflected off an object positioned outside of the nominaldetection range, and thus may ultimately serve as an indication that analternate range hypothesis is more likely to be correct.

By way of example, consider a scenario where the above-mentioned firstset of ranges was determined according to a close range hypothesis andwhere the above-mentioned second set of ranges was determined accordingto an alternate range hypothesis. In this scenario, if the furtherdetermination is that the one or more ranges do not indicate an objectpositioned outside of the nominal detection range, then the computingsystem may responsively select and use the first set of ranges forobject detection. On the other hand, if the further determination isthat the one or more ranges indicate an object positioned outside of thenominal detection range, then the computing system may responsivelyselect and use the second set of ranges for object detection. Once arange hypothesis is selected based on the further determination, thecomputing system may then engage in object detection in accordance withthe selection.

In this manner, the disclosed implementation could help reduce theextent of computation often carried out to overcome range ambiguity andto determine whether or not an object is positioned outside of thenominal detection range. For instance, the disclosed implementationcould allow a computing system to overcome range ambiguity without useof more computationally costly verification processes, such asevaluating resemblance of a range hypothesis to known object(s) and/orevaluating similarity of ranges in a range hypothesis.

In some implementations, however, the disclosed implementation couldeffectively serve as a guide for selectively triggering use of such morecomputationally costly processes. Such an approach could be advantageousbecause these processes could help overcome range ambiguity with evengreater certainty. This in turn could also help reduce the overallextent of computation, as such processes would be used more selectivelyrather than be used on a more frequent basis.

When a computing system uses the disclosed implementation as a guide forselectively triggering use of other processes, these other processescould include evaluating resemblance of a range hypothesis to knownobject(s) and/or of evaluating similarity of ranges in a rangehypothesis, as described in application Ser. No. 15/638,607, which isincorporated herein by reference. However, without departing from thescope of the present disclosure, it should be understood that thecomputing system could additionally or alternatively selectively engagein other types of processes to verify whether or not an object ispositioned outside of the nominal detection range.

In any case, when a computing system uses the disclosed implementationas a guide for selectively triggering use of other processes, thecomputing system could do so in various ways.

For instance, the computing system may trigger use of one or more otherverification processes only when light pulse detection(s) in extendeddetection period(s) indicate that an object might be positioned outsideof the nominal detection range. In particular, if the above-mentionedfurther determination is that the one or more ranges do not indicate anobject positioned outside of the nominal detection range, then thecomputing system could simply use the one or more ranges as basis forobject detection as further discussed herein, thereby possibly avoidinguse of other verification processes in such a situation. On the otherhand, if the above-mentioned further determination is that the one ormore ranges indicate an object positioned outside of the nominaldetection range, then the computing system could responsively engage inan additional process to verify whether or not an object is indeedpositioned outside of the nominal detection range.

Moreover, when the computing system engages in the additionalverification process, the computing system may do so only for adirection where an object might be positioned outside of the nominaldetection range. In particular, the LIDAR device may emit one or moreparticular light pulses in a particular direction of travel and may thendetect these particular light pulses during extended detectionperiod(s). Then, the computing system could determine range(s) for theseparticular light pulses, and may make a further determination that theserange(s) indicate that an object is positioned outside of the nominaldetection range and along the particular direction of travel. Therefore,in this scenario, the computing system may responsively engage in theadditional verification process to verify whether or not an object ispositioned outside of the nominal detection range and substantiallyalong the particular direction of travel. To do so, for example, thecomputing system may generate and evaluate multiple range hypotheses inaccordance with an additional verification process only for light pulsesdetected while the LIDAR device's detectors are oriented in theparticular direction of travel. Other examples are also possible.

In a further aspect, engaging in object detection in accordance with theabove-mentioned further determination could involve using the furtherdetermination as basis for generating a representation of an object,determining a distance to an object, and/or identifying an object, amongother possibilities.

When the computing system generates a representation of an object inaccordance with the further determination, this could involve assemblinga point cloud representative of the object. However, otherrepresentations of an object are possible as well without departing fromthe scope of the present disclosure. In any case, once the computingsystem determines one or more ranges for light pulse(s) detected duringextended detection period(s) and optionally makes the furtherdetermination of whether or not these ranges indicate an objectpositioned outside of the nominal detection range, the computing systemcould then use at least these ranges for generating a representation ofthe object.

Specifically, if the computing system determines that the one or moreranges indicate an object positioned outside of the nominal detectionrange, the computing system may responsively use at least these one ormore ranges as a basis for generating a representation of the objectpositioned outside of the nominal detection range. In some cases, thecomputing system could additionally use one or more other ranges forgenerating the representation of the object positioned outside of thenominal detection range. Generally, these other ranges could be rangesdetermined for light pulse(s) detected during other detection periods,such as during standard detection periods that are substantially closein time to the extended detection period(s) during which light pulse(s)were detected as discussed with regards to block 604, for instance.Moreover, these other range(s) to be used for generating therepresentation could be range(s) selected in accordance with any one ofthe techniques discussed herein to overcome range ambiguity. Forexample, in line with the discussion above, these other range(s) couldbe part of an alternate range hypothesis selected based on evaluation ofthe light pulse(s) detected during the extended detection period(s).

On the other hand, if the computing system determines that the one ormore ranges do not indicate that an object is positioned outside of thenominal detection range, the computing system may responsively use atleast these one or more ranges as a basis for generating arepresentation of an object positioned within the nominal detectionrange. In some cases, the computing system could additionally use one ormore other ranges for generating the representation of the objectpositioned within the nominal detection range. These other ranges couldbe ranges determined for other light pulse(s) detect during otherdetection periods. For example, in line with the discussion above, theseother range(s) could be part of a close range hypothesis selected basedon evaluation of the light pulse(s) detected during the extendeddetection period(s). Other examples are also possible.

Further, as noted, engaging in object detection in accordance with thefurther determination could involve using the further determination as abasis for determining a distance to an object.

In particular, if the further determination is that the one or moreranges indicate an object positioned outside of the nominal detectionrange, then the computing system may responsively use at least the oneor more ranges as a basis for determining a distance between the LIDARdevice and the object positioned outside of the nominal detection range.For instance, the computing system could determine this distance to beone of these ranges or to be an average of these ranges, among otheroptions.

In some cases, the computing system could additionally use one or moreother ranges for determining a distance to the object positioned outsideof the nominal detection range. For instance, these other ranges couldbe ranges of an alternate range hypothesis (i) determined for lightpulse(s) detected during standard detection periods and (ii) selectedbased on evaluation of the light pulse(s) detected during the extendeddetection period(s). As such, when determining a distance between theLIDAR device and the object positioned outside of the nominal detectionrange, the computing system could, for example, determine the distanceto be an average of all the ranges at issue, which may include (i) theranges determined for light pulses detected during extended detectionperiods and (ii) the ranges determined for light pulses detected duringstandard detection periods (e.g., according to a far range hypothesis).Other examples are also possible.

On the other hand, if the further determination is that the one or moreranges do not indicate that an object is positioned outside of thenominal detection range, then the computing system may responsively useat least the one or more ranges as a basis for determining a distancebetween the LIDAR device and an object positioned within the nominaldetection range. For instance, the computing system could determine thisdistance to be one of these ranges or to be an average of these ranges,among other options.

In some cases, the computing system could additionally use one or moreother ranges for determining a distance to the object positioned withinthe nominal detection range. For instance, these other ranges could beranges of a close range hypothesis (i) determined for light pulse(s)detected during standard detection periods and (ii) selected based onevaluation of the light pulse(s) detected during the extended detectionperiod(s). As such, when determining a distance between the LIDAR deviceand the object positioned within the nominal detection range, thecomputing system could, for example, determine the distance to be anaverage of all the ranges at issue, which may include (i) the rangesdetermined for light pulses detected during extended detection periodsand (ii) the ranges determined for light pulses detected during standarddetection periods. Other examples are also possible.

Yet further, as noted, engaging in object detection in accordance withthe above-mentioned further determination could involve using thefurther determination as basis for identifying an object.

Generally, the computing system could identify an object by determiningwhether or not a set of ranges is representative of one or more knownobjects, such as based on object recognition technique(s). For instance,the computing system could have stored on or otherwise have access to aplurality of point clouds each respectively indicative of a known object(e.g., road sign(s)). Therefore, the computing system could assemble apoint cloud based on a particular set of ranges, and could thendetermine whether or not this assembled point cloud matches at least oneof the plurality of point clouds. If the assembled point cloudsubstantially matches at least one of the plurality of point clouds,then the computing system may determine that the particular set ofranges is representative of at least one known object. Otherwise, thecomputing system may determine that the particular set of ranges is notrepresentative of at least one known object.

In any case, once the computing system determines one or more ranges forlight pulse(s) detected during extended detection period(s) and makesthe further determination of whether or not these ranges indicate anobject positioned outside of the nominal detection range, the computingsystem could then use at least these ranges for identifying an objectaccording to any feasible object identification technique.

Specifically, if the further determination is that the one or moreranges indicate an object positioned outside of the nominal detectionrange, then the computing system may responsively use at least these oneor more ranges as a basis for identifying an object positioned outsideof the nominal detection range. In some cases, the computing systemcould additionally use one or more other ranges for identifying theobject positioned outside of the nominal detection range. For instance,these other ranges could be ranges of an alternate range hypothesis (i)determined for light pulse(s) detected during standard detection periodsand (ii) selected based on evaluation of the light pulse(s) detectedduring the extended detection period(s).

On the other hand, if the further determination is that the one or moreranges do not indicate an object positioned outside of the nominaldetection range, then the computing system may responsively use at leastthese one or more ranges as a basis for identifying an object positionedwithin the nominal detection range. In some cases, the computing systemcould additionally use one or more other ranges for identifying theobject positioned within the nominal detection range. For instance,these other ranges could be ranges of a close range hypothesis (i)determined for light pulse(s) detected during standard detection periodsand (ii) selected based on evaluation of the light pulse(s) detectedduring the extended detection period(s). Other cases are also possible.

In yet a further aspect, the implementations discussed herein could bealternatively described from the perspective of light pulse emission andtime periods that respectively follow such light pulse emissions.Specifically, in line with the present disclosure, a computing systemfor a self-driving vehicle could operate a LIDAR device to emit lightpulses at emission times in accordance with an emission time sequence.The emission time sequence may include a standard time period (e.g.,associated with a nominal detection range for the LIDAR device) after amajority of emissions in the sequence and an extended time period afterat least one of the emissions in the sequence. For example, the extendedtime period could occur after an emission emitted in a direction oftravel of the vehicle. Other aspects are also possible.

FIGS. 7A-7D next illustrate example utilization of extended detectionperiod(s) in a LIDAR system.

FIG. 7A shows that the LIDAR device 200 could have an extended detectionrange 700 of 100 m that is greater than the maximum unambiguousdetection range 400 of 60 m. Generally, this extended detection range700 could be sparsely provided by extended detection period(s), such asby the extended detection period A shown in FIG. 7B. In particular, FIG.7B shows light pulses A-F emitted respectively at emission times A-F inaccordance with a time sequence #2 that includes extended detectionperiod(s). Specifically, these emission times establish an extendeddetection period A that is of a 666 ns duration as well as standarddetection periods B-F each of the same 400 ns duration.

As shown, light pulse A as well as light pulses B-E each reflect off thedistant object 404 positioned beyond the maximum unambiguous detectionrange 400 of the LIDAR device 200. However, due to light pulse A havinga corresponding extended detection period A that provides for theextended range 700 greater than the maximum unambiguous detection range400, light pulse A is detected during its corresponding extendeddetection period A.

Given this, the computing system could correctly determine a rangeassociated with detected light pulse A, even though the light pulse Areflected off the distant object 404 positioned beyond the maximumunambiguous detection range 400. Specifically, the computing system maydetermine that LIDAR device 200 detected light pulse A at a detectiontime T0 of 533 ns relative to emission time A, which corresponds to arange 702 of 80 m as shown in FIG. 7C. As shown in FIG. 7A, this rangeof 80 m is in fact the correct range at which the distant object 404 ispositioned away from the LIDAR device 200. Thus, by determining a rangefor a light pulse A detected during the extended detection period A, thecomputing system could have a basis for determining whether or not anobject might be positioned beyond the maximum unambiguous detectionrange 400, and could then engage in object detection accordingly.

By way of example, the computing system could determine that the maximumunambiguous detection range 400 of 60 m is less than the range 702 of 80m, and the computing system could responsively determine that the range702 indicates that an object is positioned beyond the maximumunambiguous detection range 400. Moreover, the computing system coulduse the range 702 to specifically determine that the distant object 404is positioned at 80 m away from the LIDAR device 200. In turn, thecomputing system could then operate the vehicle 300 based at least onthe determination that the distant object 404 is positioned at 80 m awayfrom the LIDAR device 200, such by navigating the vehicle 300 accordingto the presence (and possibly identification) of the distant object 404(e.g., a road sign), among other options.

Furthermore, as shown in FIG. 7B, light pulses B-E each reflect off thedistant object 404 and, as a result, are each respectively detectedduring a subsequent standard detection period, and thus the computingsystem could generate multiple range hypotheses in line with thediscussion above. In particular, the computing system could determinecandidate ranges associated with detected light pulses B-E withoutaccounting for the possibility of large retroreflective object(s)located beyond the maximum unambiguous detection range.

For instance, the computing system may determine that the LIDAR device200 detected light pulse B at a detection time Tn0 of 133 ns relative toemission time C, which corresponds to a range of 20 m as shown in FIG.7D. And as indicated by detection times Tn1 to Tn4, a similar approachcould be used for determining ranges associated with light pulses C-E,thereby resulting in first ranges 704 corresponding to a close rangehypothesis of an object being positioned at 20 m away from the LIDARdevice 200. Additionally, the computing system may determine that theLIDAR device 200 detected light pulse B at a detection time Tf1 of 533ns relative to emission time B, which corresponds to a range of 80 m asshown in FIG. 7D. And as indicated by detection times Tf2 to Tf4, asimilar approach could be used for determining ranges associated withlight pulses C-E, thereby resulting in second ranges 706 correspondingto a far range hypothesis of an object being positioned at 80 m awayfrom the LIDAR device 200.

In accordance with the present disclosure, the computing system coulddetermine which of these range hypotheses is likely correct based onevaluation of the light pulse detection during an extended detectionperiod. In particular, based on the determined range 702 for the lightpulse A detected during extended detection period A and based onstandard detection periods B-F being substantially close in time to theextended detection period A, the computing system could use thedetermined range 702 as basis for selecting between use of ranges 704for object detection and use of ranges 706 for object detection. Indoing so, as illustrated by FIG. 7D, the computing system could make adetermination that range 702 indicates an object is positioned beyondthe maximum unambiguous detection range 400, and could responsivelyselect use of ranges 706 rather than ranges 704, as ranges 706 aregreater than the maximum unambiguous detection range 400 and ranges 704are less than the maximum unambiguous detection range 400.

Once the computing system selects use of ranges 706 for purposes objectdetection, the computing system could then engage in further objectdetection accordingly. For example, the computing system could assemblea point cloud based on a combination of range 702 and selected ranges706. Moreover, the computing system could then use the assembled pointcloud as a basis for identifying an object. Other examples andillustrations are also possible.

VI. Controlling a Vehicle Based on Scans by the LIDAR Device

As noted, a computing system may operate a vehicle based on scansreceived from the LIDAR device disclosed herein. In particular, thecomputing system may receive from the LIDAR device scans of anenvironment around the vehicle. And the computing system may operate thevehicle based at least on the scans of the environment received from theLIDAR device.

More specifically, the computing system may operate the LIDAR device 100to emit light into the environment. Also, the computing system mayreceive from the LIDAR device 100 data representative of detections ofreflected light. And by comparing detected light beams with emittedlight beams, the computing system may determine at least one aspect ofone or more objects in the environment.

For example, by comparing a time when a plurality of light beams wereemitted by the transmitter of the LIDAR device 100 and a time when thereceiver of the LIDAR device 100 detected reflected light, a distancebetween the LIDAR device 100 and an object in the environment may bedetermined. In other examples, aspects such as shape, color, material,etc. may also be determined based on various comparisons between emittedlight and detected light.

With this arrangement, the computing system could determine athree-dimensional (3D) representation of the environment based on datafrom the LIDAR device 100. For example, the 3D representation may begenerated by the computing system as a 3D point cloud based on the datafrom the LIDAR device 100. Each point of the 3D cloud, for example, maybe associated with a reflected light pulse. As such, the computingsystem may (e.g., continuously or from time-to-time) generate 3Drepresentations of the environment or portions thereof. And thecomputing system could then control operation of the vehicle based onevaluation of such 3D representations of the environment.

By way of example, the vehicle may be operated in an autonomous mode. Inthis example, the computing system may utilize 3D representations tonavigate the vehicle (e.g., adjust speed, direction, etc.) safely byavoiding obstacles among other possibilities. The obstacles or objects,for example, may be detected and/or identified using an image processingalgorithm or other computing method to analyze the 3D representationsand detect and/or identify the various obstacles or objects. As anotherexample, the vehicle may be operated in a partially autonomous or manualmode. In this example, the vehicle may notify a driver or operator ofthe vehicle of the presence or distance to various objects or changingroad conditions (e.g., street lights, street signs, etc.), such as bycausing a display or a speaker in the vehicle to present informationregarding one or more objects in the environment. Other examples arepossible as well.

FIG. 8 next illustrates example operation of the vehicle 300 based onscans of an environment 800 received from the LIDAR device 200. Inaccordance with the present disclosure, the vehicle's computing systemmay use data received from the LIDAR device 200 to detect and identifydistant object(s), such as a road sign 404 for example. In this regard,the computing system may determine based on the data that the road sign404 is representative of an exit that the vehicle 300 should ideallytake in order to arrive at a desired destination. In response to makingthat determination, the computing system may then operate the vehicle300 to switch from driving on lane 1 to driving on lane 2.

In practice, the computing system may distinguish between these lanes byrecognizing lane markers within 3D representations of the environment800. For instance, the vehicle's computing system may use data receivedfrom the LIDAR device 200 to detect and identify the nearby lane markerthat separates lane 1 from lane 2. Moreover, before operating thevehicle to switch lanes, the computing system may scan the environmentto detect and identify objects, so that computing system can operate thevehicle 300 in a way that avoids those detected/identified objects whilealso operating the vehicle 300 to switch lanes.

For instance, the computing system may use data received from the LIDARdevice 200 to detect and identify the nearby vehicle 802 as well as todetect and identify road sign 402. Based on thosedetections/identifications, the computing system may operate the vehicle300 in a way that avoids the vehicle 802 and road sign 402 while alsooperating the vehicle 300 to switch from driving on lane 1 to driving onlane 2. Other illustrations are possible as well.

VII. Example Arrangement of a Vehicle

Finally, FIG. 9 is a simplified block diagram of a vehicle 900,according to an example embodiment. The vehicle 900 may be similar tothe vehicle 300, and may include a LIDAR device similar to the LIDARdevice 100. Further, the vehicle 900 may be configured to performfunctions and methods herein such as method 800 and/or method 1000. Asshown, the vehicle 900 includes a propulsion system 902, a sensor system904, a control system 906 (could also be referred to as a controller906), peripherals 908, and a computer system 910. Vehicle 900 may be,for example, a motor vehicle, railed vehicle, watercraft, or aircraft.In other embodiments, the vehicle 900 may include more, fewer, ordifferent systems, and each system may include more, fewer, or differentcomponents.

Additionally, the systems and components shown may be combined ordivided in any number of ways. For instance, the control system 906 andthe computer system 910 may be combined into a single system thatoperates the vehicle 900 in accordance with various operations.

The propulsion system 902 may be configured to provide powered motionfor the vehicle 900. As shown, the propulsion system 902 includes anengine/motor 918, an energy source 920, a transmission 922, andwheels/tires 924.

The engine/motor 918 may be or include any combination of an internalcombustion engine, an electric motor, a steam engine, and a Stirlingengine. Other motors and engines are possible as well. In someembodiments, the propulsion system 902 may include multiple types ofengines and/or motors. For instance, a gas-electric hybrid car mayinclude a gasoline engine and an electric motor. Other examples arepossible.

The energy source 920 may be a source of energy that powers theengine/motor 918 in full or in part. That is, the engine/motor 918 maybe configured to convert the energy source 920 into mechanical energy.Examples of energy sources 920 include gasoline, diesel, propane, othercompressed gas-based fuels, ethanol, solar panels, batteries, and othersources of electrical power. The energy source(s) 920 may additionallyor alternatively include any combination of fuel tanks, batteries,capacitors, and/or flywheels. In some embodiments, the energy source 920may provide energy for other systems of the vehicle 900 as well.

The transmission 922 may be configured to transmit mechanical power fromthe engine/motor 918 to the wheels/tires 924. To this end, thetransmission 922 may include a gearbox, clutch, differential, driveshafts, and/or other elements. In embodiments where the transmission 922includes drive shafts, the drive shafts may include one or more axlesthat are configured to be coupled to the wheels/tires 924.

The wheels/tires 924 of vehicle 900 may be configured in variousformats, including a bicycle/motorcycle, tricycle, car/truck four-wheelformat, or a rail. Other wheel/tire formats are possible as well, suchas those including six or more wheels. In any case, the wheels/tires 924may be configured to rotate differentially with respect to otherwheels/tires 924. In some embodiments, the wheels/tires 924 may includeat least one wheel that is fixedly attached to the transmission 922 andat least one tire coupled to a rim of the wheel that could make contactwith the driving surface. The wheels/tires 924 may include anycombination of metal and rubber, or combination of other materials. Thepropulsion system 902 may additionally or alternatively includecomponents other than those shown.

The sensor system 904 may include a number of sensors configured tosense information about an environment in which the vehicle 900 islocated, as well as one or more actuators 936 configured to modify aposition and/or orientation of the sensors. As shown, the sensors of thesensor system 904 include a Global Positioning System (GPS) 926, aninertial measurement unit (IMU) 928, a RADAR unit 930, a laserrangefinder and/or LIDAR unit 932, and a camera 934. The sensor system904 may include additional sensors as well, including, for example,sensors that monitor internal systems of the vehicle 900 (e.g., an O₂monitor, a fuel gauge, an engine oil temperature, etc.). Other sensorsare possible as well.

The GPS 926 may be any sensor (e.g., location sensor) configured toestimate a geographic location of the vehicle 900. To this end, the GPS926 may include a transceiver configured to estimate a position of thevehicle 900 with respect to the Earth. The GPS 926 may take other formsas well.

The IMU 928 may be any combination of sensors configured to senseposition and orientation changes of the vehicle 900 based on inertialacceleration. In some embodiments, the combination of sensors mayinclude, for example, accelerometers and gyroscopes. Other combinationsof sensors are possible as well.

The RADAR unit 930 may be any sensor configured to sense objects in theenvironment in which the vehicle 900 is located using radio signals. Insome embodiments, in addition to sensing the objects, the RADAR unit 930may additionally be configured to sense the speed and/or heading of theobjects.

Similarly, the laser range finder or LIDAR unit 932 may be any sensorconfigured to sense objects in the environment in which the vehicle 900is located using lasers. For example, LIDAR unit 932 may include one ormore LIDAR devices, at least some of which may take the form the LIDARdevice 100 disclosed herein.

The camera 934 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which thevehicle 900 is located. To this end, the camera may take any of theforms described above. The sensor system 904 may additionally oralternatively include components other than those shown.

The control system 906 may be configured to control operation of thevehicle 900 and its components. To this end, the control system 906 mayinclude a steering unit 938, a throttle 940, a brake unit 942, a sensorfusion algorithm 944, a computer vision system 946, a navigation orpathing system 948, and an obstacle avoidance system 950.

The steering unit 938 may be any combination of mechanisms configured toadjust the heading of vehicle 900. The throttle 940 may be anycombination of mechanisms configured to control the operating speed ofthe engine/motor 918 and, in turn, the speed of the vehicle 900. Thebrake unit 942 may be any combination of mechanisms configured todecelerate the vehicle 900. For example, the brake unit 942 may usefriction to slow the wheels/tires 924. As another example, the brakeunit 942 may convert the kinetic energy of the wheels/tires 924 toelectric current. The brake unit 942 may take other forms as well.

The sensor fusion algorithm 944 may be an algorithm (or a computerprogram product storing an algorithm) configured to accept data from thesensor system 904 as an input. The data may include, for example, datarepresenting information sensed at the sensors of the sensor system 904.The sensor fusion algorithm 944 may include, for example, a Kalmanfilter, a Bayesian network, an algorithm for some of the functions ofthe methods herein, or any other algorithm. The sensor fusion algorithm944 may further be configured to provide various assessments based onthe data from the sensor system 904, including, for example, evaluationsof individual objects and/or features in the environment in which thevehicle 900 is located, evaluations of particular situations, and/orevaluations of possible impacts based on particular situations. Otherassessments are possible as well.

The computer vision system 946 may be any system configured to processand analyze images captured by the camera 934 in order to identifyobjects and/or features in the environment in which the vehicle 900 islocated, including, for example, traffic signals and obstacles. To thisend, the computer vision system 946 may use an object recognitionalgorithm, a Structure from Motion (SFM) algorithm, video tracking, orother computer vision techniques. In some embodiments, the computervision system 946 may additionally be configured to map the environment,track objects, estimate the speed of objects, etc.

The navigation and pathing system 948 may be any system configured todetermine a driving path for the vehicle 900. The navigation and pathingsystem 948 may additionally be configured to update the driving pathdynamically while the vehicle 900 is in operation. In some embodiments,the navigation and pathing system 948 may be configured to incorporatedata from the sensor fusion algorithm 944, the GPS 926, the LIDAR unit932, and one or more predetermined maps so as to determine the drivingpath for vehicle 900.

The obstacle avoidance system 950 may be any system configured toidentify, evaluate, and avoid or otherwise negotiate obstacles in theenvironment in which the vehicle 900 is located. The control system 906may additionally or alternatively include components other than thoseshown.

Peripherals 908 may be configured to allow the vehicle 900 to interactwith external sensors, other vehicles, external computing devices,and/or a user. To this end, the peripherals 908 may include, forexample, a wireless communication system 952, a touchscreen 954, amicrophone 956, and/or a speaker 958.

The wireless communication system 952 may be any system configured towirelessly couple to one or more other vehicles, sensors, or otherentities, either directly or via a communication network. To this end,the wireless communication system 952 may include an antenna and achipset for communicating with the other vehicles, sensors, servers, orother entities either directly or via a communication network. Thechipset or wireless communication system 952 in general may be arrangedto communicate according to one or more types of wireless communication(e.g., protocols) such as Bluetooth, communication protocols describedin IEEE 802.11 (including any IEEE 802.11 revisions), cellulartechnology (such as GSM, CDMA, UMTS, EV-DO, WiMAX, or LTE), Zigbee,dedicated short range communications (DSRC), and radio frequencyidentification (RFID) communications, among other possibilities. Thewireless communication system 952 may take other forms as well.

The touchscreen 954 may be used by a user to input commands to thevehicle 900. To this end, the touchscreen 954 may be configured to senseat least one of a position and a movement of a user's finger viacapacitive sensing, resistance sensing, or a surface acoustic waveprocess, among other possibilities. The touchscreen 954 may be capableof sensing finger movement in a direction parallel or planar to thetouchscreen surface, in a direction normal to the touchscreen surface,or both, and may also be capable of sensing a level of pressure appliedto the touchscreen surface. The touchscreen 954 may be formed of one ormore translucent or transparent insulating layers and one or moretranslucent or transparent conducting layers. The touchscreen 954 maytake other forms as well.

The microphone 956 may be configured to receive audio (e.g., a voicecommand or other audio input) from a user of the vehicle 900. Similarly,the speakers 958 may be configured to output audio to the user of thevehicle 900. The peripherals 908 may additionally or alternativelyinclude components other than those shown.

The computer system 910 may be configured to transmit data to, receivedata from, interact with, and/or control one or more of the propulsionsystem 902, the sensor system 904, the control system 906, and theperipherals 908. To this end, the computer system 910 may becommunicatively linked to one or more of the propulsion system 902, thesensor system 904, the control system 906, and the peripherals 908 by asystem bus, network, and/or other connection mechanism (not shown).

In one example, the computer system 910 may be configured to controloperation of the transmission 922 to improve fuel efficiency. As anotherexample, the computer system 910 may be configured to cause the camera934 to capture images of the environment. As yet another example, thecomputer system 910 may be configured to store and execute instructionscorresponding to the sensor fusion algorithm 944. As still anotherexample, the computer system 910 may be configured to store and executeinstructions for determining a 3D representation of the environmentaround the vehicle 900 using the LIDAR unit 932. Other examples arepossible as well. Thus, the computer system 910 could function as thecontroller for the LIDAR unit 932.

As shown, the computer system 910 includes the processor 912 and datastorage 914. The processor 912 may include one or more general-purposeprocessors and/or one or more special-purpose processors. To the extentthe processor 912 includes more than one processor, such processorscould work separately or in combination. Data storage 914, in turn, mayinclude one or more volatile and/or one or more non-volatile storagecomponents, such as optical, magnetic, and/or organic storage, and datastorage 914 may be integrated in whole or in part with the processor912.

In some embodiments, data storage 914 may contain instructions 916(e.g., program logic) executable by the processor 912 to execute variousvehicle functions (e.g., method 500, etc.). Data storage 914 may containadditional instructions as well, including instructions to transmit datato, receive data from, interact with, and/or control one or more of thepropulsion system 902, the sensor system 904, the control system 906,and/or the peripherals 908. The computer system 910 may additionally oralternatively include components other than those shown.

As shown, the vehicle 900 further includes a power supply 960, which maybe configured to provide power to some or all of the components of thevehicle 900. To this end, the power supply 960 may include, for example,a rechargeable lithium-ion or lead-acid battery. In some embodiments,one or more banks of batteries could be configured to provide electricalpower. Other power supply materials and configurations are possible aswell. In some embodiments, the power supply 960 and energy source 920may be implemented together as one component, as in some all-electriccars.

In some embodiments, the vehicle 900 may include one or more elements inaddition to or instead of those shown. For example, the vehicle 900 mayinclude one or more additional interfaces and/or power supplies. Otheradditional components are possible as well. In such embodiments, datastorage 914 may further include instructions executable by the processor912 to control and/or communicate with the additional components.

Still further, while each of the components and systems are shown to beintegrated in the vehicle 900, in some embodiments, one or morecomponents or systems may be removably mounted on or otherwise connected(mechanically or electrically) to the vehicle 900 using wired orwireless connections. The vehicle 900 may take other forms as well.

VIII. Conclusion

The particular arrangements shown in the Figures should not be viewed aslimiting. It should be understood that other implementations may includemore or less of each element shown in a given Figure. Further, some ofthe illustrated elements may be combined or omitted. Yet further, anexemplary implementation may include elements that are not illustratedin the Figures.

Additionally, while various aspects and implementations have beendisclosed herein, other aspects and implementations will be apparent tothose skilled in the art. The various aspects and implementationsdisclosed herein are for purposes of illustration and are not intendedto be limiting, with the true scope and spirit being indicated by thefollowing claims. Other implementations may be utilized, and otherchanges may be made, without departing from the spirit or scope of thesubject matter presented herein. It will be readily understood that theaspects of the present disclosure, as generally described herein, andillustrated in the figures, can be arranged, substituted, combined,separated, and designed in a wide variety of different configurations,all of which are contemplated herein.

1-20. (canceled)
 21. A method comprising: operating a Light Detectionand Ranging (LIDAR) device to emit light pulses in accordance with anemission time sequence and to detect return light pulses in accordancewith a detection time sequence, wherein the detection time sequenceincludes, for each emitted light pulse in the emission time sequence, acorresponding detection period for detection of a corresponding returnlight pulse, wherein the corresponding detection period is one of (i) astandard detection period having a duration that establishes a nominaldetection range for the LIDAR device or (ii) an extended detectionperiod having a duration longer than the duration of the standarddetection period; adjusting the detection time sequence, whereinadjusting the detection time sequence comprises increasing utilizationof extended detection periods; and operating the LIDAR device to detectreturn light pulses according to the adjusted detection time sequence.22. The method of claim 21, wherein increasing utilization of extendeddetection periods comprises enabling one or more extended detectionperiods.
 23. The method of claim 21, wherein increasing utilization ofextended detection periods comprises extending one or more detectionperiods.
 24. The method of claim 21, wherein increasing utilization ofextended detection periods comprises increasing how frequently extendedtime periods occur.
 25. The method of claim 21, wherein the detectiontime sequence comprises a schedule specifying when standard detectionperiods occur and when extended detection periods occur, and whereinadjusting the detection time sequence comprises changing the schedule.26. The method of claim 21, wherein the LIDAR device is coupled to avehicle, wherein adjusting the detection time sequence comprisesadjusting the detection time sequence based on information about anenvironment of the vehicle.
 27. The method of claim 26, wherein theinformation about the environment of the vehicle comprises data from asensor system of the vehicle.
 28. The method of claim 27, wherein thesensor system comprises at least one of a Global Positioning System(GPS), an inertial measurement unit (IMU), a RADAR unit, a LIDAR unit,or a camera.
 29. The method of claim 26, wherein adjusting the detectiontime sequence based on information about the environment of the vehiclecomprises: receiving data indicative of a location of the vehicle;determining that the vehicle is entering a highway based on the dataindicative of the location of the vehicle; and adjusting the detectiontime sequence responsive to determining that the vehicle is entering thehighway.
 30. The method of claim 29, wherein receiving data indicativeof the location of the vehicle comprises receiving data from a GlobalPositioning System (GPS).
 31. A computing system comprising: one or moreprocessors; a non-transitory computer readable medium; and programinstructions stored on the non-transitory computer readable medium andexecutable by the one or more processors to perform operationscomprising: operating a Light Detection and Ranging (LIDAR) device toemit light pulses in accordance with an emission time sequence and todetect return light pulses in accordance with a detection time sequence,wherein the detection time sequence includes, for each emitted lightpulse in the emission time sequence, a corresponding detection periodfor detection of a corresponding return light pulse, wherein thecorresponding detection period is one of (i) a standard detection periodhaving a duration that establishes a nominal detection range for theLIDAR device or (ii) an extended detection period having a durationlonger than the duration of the standard detection period; adjusting thedetection time sequence, wherein adjusting the detection time sequencecomprises increasing utilization of extended detection periods; andoperating the LIDAR device to detect return light pulses according tothe adjusted detection time sequence.
 32. The computing system of claim31, wherein increasing utilization of extended detection periodscomprises enabling one or more extended detection periods.
 33. Thecomputing system of claim 31, wherein increasing utilization of extendeddetection periods comprises extending one or more detection periods. 34.The computing system of claim 31, wherein increasing utilization ofextended detection periods comprises increasing how frequently extendedtime periods occur.
 35. The computing system of claim 31, wherein thedetection time sequence comprises a schedule specifying when standarddetection periods occur and when extended detection periods occur, andwherein adjusting the detection time sequence comprises changing theschedule.
 36. The computing system of claim 31, wherein the LIDAR deviceis coupled to a vehicle, wherein adjusting the detection time sequencecomprises adjusting the detection time sequence based on informationabout an environment of the vehicle.
 37. A vehicle comprising: a LightDetection and Ranging (LIDAR) device; and a computing system configuredto perform operations comprising: operating the LIDAR device to emitlight pulses in accordance with an emission time sequence and to detectreturn light pulses in accordance with a detection time sequence,wherein the detection time sequence includes, for each emitted lightpulse in the emission time sequence, a corresponding detection periodfor detection of a corresponding return light pulse, wherein thecorresponding detection period is one of (i) a standard detection periodhaving a duration that establishes a nominal detection range for theLIDAR device or (ii) an extended detection period having a durationlonger than the duration of the standard detection period; adjusting thedetection time sequence, wherein adjusting the detection time sequencecomprises increasing utilization of extended detection periods; andoperating the LIDAR device to detect return light pulses according tothe adjusted detection time sequence.
 38. The vehicle of claim 37,wherein adjusting the detection time sequence comprises adjusting thedetection time sequence based on information about an environment of thevehicle.
 39. The vehicle of claim 38, further comprising a sensorsystem, wherein the information about the environment of the vehiclecomprises data from the sensor system.
 40. The vehicle of claim 39,wherein the sensor system comprises at least one of a Global PositioningSystem (GPS), an inertial measurement unit (IMU), a RADAR unit, a LIDARunit, or a camera.